Mary Elizabeth Sutherland How Nature will make a systematic investment of effort into.. Network Science Institute 2019-10-09 | Mary Elizabeth Sutherland is a senior editor where she handles manuscripts in the behavioral sciences. She previously handled manuscripts spanning the same general disciplines and topics at Nature Human Behaviour, as a senior editor, and at Nature Communications, as an associate editor. Prior to her editorial career, Mary Elizabeth obtained a PhD in cognitive neuropsychology from McGill University, where she worked in auditory cognitive neuroscience with Dr. Robert Zatorre. She continued her training both at the Max Planck Institute for Human and Cognitive Brain Sciences in Leipzig and at the Catholic University, in Santiago Chile. She was briefly a professor at this latter institute, in a new position that the university created to span the medical and social sciences, before she realized that she would be a better editor than researcher and moved back to New York to take the editorial position at Nature Communications.
Cristopher Moore on Tensor Networks and Phase Transitions in Machine Learning Network Science Institute 2024-10-13 | Date: 10/10/24 Abstract: Suppose we observe a matrix of data with a low-rank “signal” obscured by noise. The standard way to find the signal, at least approximately, is PCA (principal component analysis): just look at the eigenvectors of the matrix. For Gaussian noise, random matrix theory tells us exactly how well this works: that is, the accuracy we can achieve as a function of the signal-to-noise ratio. For tensors, such as three-index tables A_{ijk}, the situation is much more complex. Here there seems to be a “statistical-computational gap,” namely a regime where finding the signal is possible but exponentially hard. Physically, this corresponds to a “glass transition,” where the optimum becomes hidden behind an energy barrier. Mathematically, it means that we believe no polynomial-time algorithm exists, and that exhaustive search is necessary. I’ll give evidence for this exponential hardness by showing that no algorithm remotely similar to PCA can work. Along the way, I’ll give an introduction to tensor networks — a generalization of matrix products and traces thaat everyone, including network theorists, should know about.Biography: Cristopher Moore received his B.A. in Physics, Mathematics, and Integrated Science from Northwestern University, and his Ph.D. in Physics from Cornell. From 2000 to 2012 he was a professor at the University of New Mexico, with joint appointments in Computer Science and Physics. Since 2012, Moore has been a resident professor at the Santa Fe Institute; he has also held visiting positions at École Normale Superieure, École Polytechnique, Université Paris 7, École Normale Superieure de Lyon, Northeastern University, the University of Michigan, and Microsoft Research. He has written 160 papers at the boundary between mathematics, physics, and computer science, ranging from quantum computing, social networks, and phase transitions in NP-complete problems and Bayesian inference, to risk assessment in criminal justice. He is an elected Fellow of the American Physical Society, the American Mathematical Society, and the American Association for the Advancement of Science. With Stephan Mertens, he is the author of The Nature of Computation from Oxford University Press.
Jürgen Jost on Curvature and Data Network Science Institute 2024-10-04 | Date: 10/03/24Abstract: Many data are metric, that is, with distances between data points. Therefore, metric geometry should provide tools for their analysis. Since the most important concept of geometry is curvature, many curvature concepts have been proposed and developed in metric geometry. I will explain those concepts systematically and show how they can yield insight into data from a wide range of domains.Bio: Jürgen Jost is a Professor of Mathematics and Director of the Max Planck Institute for Mathematics in the Sciences (MPI-MiS) in Leipzig, Germany, and an External Professor at the Santa Fe Institute in New Mexico, USA. He was born in Münster, Germany, in 1956. He studied mathematics, physics, economics and philosophy at the University of Bonn from 1975 to 1980, and in 1980 he also completed his PhD in mathematics at the same university. He has held various postdoctoral and visiting positions at IAS Princeton, UC San Diego, ANU Canberra, MSRI Berkeley, Harvard, ETH Zürich and IHES Paris. From 1984 to 1996 he was Professor of Mathematics at the Ruhr University Bochum, and in 1996 he moved to Leipzig, where together with Eberhard Zeidler and Stefan Müller, he founded the MPI-MiS. To date, Jürgen Jost has written more than 600 research articles and more than 20 books, spanning many different areas of mathematics and applied sciences, as well as philosophy and history of science. He has supervised more than 60 PhD students and numerous postdocs.
Giulia Pullano on Integration of High-Resolution Mobility Data Network Science Institute 2024-09-24 | Date: 09/23/24Abstract: Human mobility is a critical driver of epidemics by substantially altering the probability of encounters, patterns of exposure, and the likelihood of disease propagation. While long-range movements may shape patterns of pathogen importation, short-range mobility and contact structures amplify local epidemics. Characterizing mobility patterns and social mixing across scales is therefore essential for understanding why and how epidemics emerge and spread, as well as for developing effective prevention and control strategies. The COVID-19 crisis, sparked a data-sharing revolution, with network operators such as Orange and Telefonica, along with tech giants like Google, Apple, and Facebook, providing real-time aggregated mobility data from mobile phone traces to track human mobility and help fight the pandemic. Epidemiological research is now focused on developing novel mathematical and computational frameworks to integrate high-resolution mobility data into models, enabling both retrospective analyses and real-time epidemic monitoring. In my talk, I will discuss how we utilized these data during the early stages of COVID-19 in France to capture the dynamic shifts in social mixing caused by mobility interventions and address critical public health questions. Additionally, I will present a retrospective theoretical study that characterizes the mobility factors shaping geographical diffusion across scales in the United States and demonstrates a model designed to optimize reliability for outbreak response while balancing mobility data requirements.Bio: Dr. Giulia Pullano is a postdoctoral fellow at Georgetown University in Washington, DC, USA, working in the Bansal Lab within the Biology Department. Her research focuses on developing mathematical and computational models to understand the geographical dynamics of human-to-human diseases and inform public health policies. She is particularly interested in characterizing seasonal patterns in human behavior and disruptions during epidemics or extreme events to integrate them into epidemic models and optimize public health interventions. From 2020 to 2022, Dr. Pullano has been actively involved in the COVID-19 pandemic response, advising French public health agencies and government authorities. Dr. Pullano earned her PhD in Biomathematics and Public Health from the French National Institute of Health and Medical Research (INSERM), Sorbonne University, and Orange S.A., under the supervision of Dr. Vittoria Colizza. She obtained a Master’s degree in Physics of Complex Systems from Università degli Studi di Torino in 2016 and a Bachelor's degree in Physics from Università degli Studi di Roma La Sapienza in 2014.
Silvia De Sojo on The Gender Gaps in Human Mobility Network Science Institute 2024-09-20 | Date: 09/19/2024Abstract: There is a gender gap in human mobility, with women travelling shorter distances, visiting fewer unique locations, and exhibiting lower physical activity levels compared to men. Previous studies in geography, transportation, social sciences, and - more recently - in quantitative studies in Human Mobility, have emphasized the need to study the behavioral heterogeneities in mobility and explore human mobility from a gendered perspective. Human mobility is characterized by a remarkable regularity and predictability, largely driven by work-related commutes. Work often defines the need to be at a specific place (the workplace) at specific times (work hours) and for a fixed duration (the workday). This has led researchers to hypothesize that the notable gender differences in the labor market might underpin the observed differences in mobility patterns between men and women.In this talk, we will examine the impact of work constraints and gender on human mobility using a large-scale dataset that captures the movements of 600.000 individuals who self-declared as female or male, spanning ten countries. We will explore well-known mobility metrics and the differences in the structure of individuals' networks of visited locations. Finally, I will show that gender differences in mobility persist even when work constraints are accounted for, suggesting that other factors—such as family obligations and societal norms— may play a role in shaping the gender differences in mobility.Bio: Silvia is a PhD student at the Technical University of Denmark (DTU) and a member of the Social Complexity Lab, led by Laura Alessandretti and Sune Lehmann. Currently, she is a Visiting Scholar at the MIT Senseable City Lab, where she is be based until January 2025. Her research focuses on aspects of human online and offline behaviour using large-scale data, and methods from Complex Systems and Computational Social Science. A key aspect of her PhD work is investigating behavioral inequalities, with a particular emphasis on the gender differences in human mobility.
Alina Starovolsky Shitrit on Value Transmission on TikTok (Adolescence) Network Science Institute 2024-09-13 | Date: 09/10/24Abstract: Value Transmission on TikTok (Adolescence) Values are essential life goals that shape an individual's identity, choices, attitudes, and behaviors. Traditionally transmitted primarily through parents, value communication is undergoing a transformation with the rise of social media platforms like TikTok, which is now used by 67% of teenagers, with 50% engaging almost constantly. While much research on social media influencers has focused on marketing, the values conveyed through TikTok content remain underexplored. This presentation examines the values present in TikTok posts, remain underexplored. This presentation examines the values present in TikTok posts, the strategies influencers use to communicate them, and how adolescents perceive and adopt these values. We manually coded nearly 1,000 posts from 100 influencers across various genres for 19 values based on Schwartz’s framework and identified different communicative strategies. Additionally, we are developing an NLP tool to predict the values transmitted in TikTok content, allowing us to expand our datas and deepen our understanding of how these values influence today's youth. Our findings provide critical insights into how TikTok shapes adolescents' value systems, offering a fresh perspective on digital value transmission in the social media age.Bio: I have a background in Computer Science and Bioinformatics, with over a decade of experience in the biotech industry, specializing in drug development and big data analysis. After earning a PhD with a focus on Next Generation Sequencing, I transitioned into education research. My current postdoctoral work explores intersections between computational methods and social science, particularly in understanding child development.
Amanda Perofsky on Impacts of human mobility Network Science Institute 2024-09-10 | Date: 09/9/24Abstract: While aggregated mobile device location data have been extensively used to model SARS-CoV-2 dynamics, relationships between mobility behavior and the transmission of other respiratory pathogens are less understood. Understanding the influence of human mobility on endemic pathogens is crucial for predictive purposes, especially as perturbed circulation can lead to overlapping epidemics of different pathogens, putting extreme strain on healthcare systems. In this seminar, I will present research investigating the effects of population behavior on the transmission of 17 endemic viruses and SARS-CoV-2 in Seattle, Washington, during pre- and post-pandemic years, using detailed data from a citywide respiratory pathogen surveillance study and high-resolution cellphone mobility data. I will highlight mobility metrics that are consistent leading indicators of outbreaks and compare patterns across pathogens with different transmission modes, seasonal cycles, and age distributions of infection. Additionally, I will discuss recent work linking the evolutionary and epidemiological dynamics of influenza in the US and future plans to explore the effects of decreased social distancing and waning immunity on the post-pandemic reemergence of respiratory syncytial virus (RSV) in Seattle.Bio: Dr. Amanda Perofsky is a research scientist in the Brotman Baty Institute for Precision Medicine at the University of Washington. Prior to joining UW, she completed her PhD in Ecology, Evolution, and Behavior with Dr. Lauren Ancel Meyers at the University of Texas at Austin and a postdoctoral fellowship with Dr. Cécile Viboud at the Fogarty International Center, US National Institutes of Health. Dr. Perofsky’s research focuses on the ecological, evolutionary, and behavioral drivers of respiratory virus infections, with aims to improve infectious disease surveillance and better understand and predict recurring and emerging outbreaks. She applies statistical and computational approaches to study respiratory virus transmission patterns and epidemiology, with a particular focus on influenza and SARS-CoV-2. She also produces operational forecasts and projections of respiratory virus outbreaks.
Sara Venturini on Optimization Methods for Community Detection and Graph Semi-Supervised Learning Network Science Institute 2024-09-03 | Date: 07/12/24Abstract: Community detection is one of the most relevant tasks in the analysis of graphs as it has been shown that many real-world networks show a community structure. While many community detection algorithms have been developed over the recent years, most of these are designed for standard single-layer graphs. However, this can be an oversimplification of reality. In the first part of the talk, we will deal with the community detection and graph semi-supervised learning issues extended to multiplex networks, i.e., networks with multiple layers having same node sets and no inter-layer connections. The contributions are both in the problems' formulation and in their resolution applying and adapting suited and tailored optimization methods. In the second part of the talk, we will focus on the analysis of collaborations between scholars. Collaboration is crucial for deepening existing knowledge and gaining exposure to new ideas. We will investigate how researchers influence each other with their research topics, and how the COVID-19 pandemic affected researcher collaborations.Bio: Sara Venturini is currently a Postdoctoral Fellow at the MIT Senseable City Lab. She earned a Ph.D. in Computational Mathematics in 2023 from the University of Padova, where she started her academic career with a Bachelor’s and Master’s in Mathematics. In 2022, she won a fellowship within the AccelNet-MultiNet program, enabling her to visit Indiana University in Bloomington. Currently, she is interested in combining her computational and applied mathematics background with her passion for complex networks in real-world social science applications. Sara’s current research interests include higher-order networks, optimization methods, machine learning, and the science of science.
Jean-Gabriel Young on What can we learn from low-dimensional representations of networks? Network Science Institute 2024-09-03 | Date: 03/22/24Abstract: Machine learning systems now routinely use embeddings in thousands of dimensions to extract patterns from large-scale network data. Should we embrace this data revolution and let go of the simpler theories of yore—the likes of the S1 and Bradley-Terry models? In this talk, I will argue that low-dimensional embedding can find concise, interpretable patterns in networks and thus have a place in any modern data science stack. I will illustrate this point through a number of stories about social hierarchies and decision-making.Bio: Jean-Gabriel Young is an Assistant Professor of Mathematics and Statistics at The University of Vermont, where he also holds faculty affiliations at the Translational Global Infectious Diseases Research Center and the Vermont Complex Systems Center. Professor Young’s research is at the intersection of statistical inference, epidemiology, and complex systems. Previously, he was a James S. McDonnell Foundation Fellow at the Center for the Study of Complex Systems of the University of Michigan, mentored by Professor Mark Newman. He obtained his PhD in Physics from Université Laval, under the guidance of Prof. Louis J. Dubé and Prof. Patrick Desrosiers.
Sune Lehmann on Using sequences of life-events to predict human lives Network Science Institute 2024-09-03 | Date: 07/11/24Abstract: Here we represent human lives in a way that shares structural similarity to language, and we exploit this similarity to adapt natural language processing techniques to examine the evolution and predictability of human lives based on detailed event sequences. We do this by drawing on a comprehensive registry dataset, which is available for Denmark across several years, and that includes information about life-events related to health, education, occupation, income, address and working hours, recorded with day-to-day resolution. We create embeddings of life-events in a single vector space, showing that this embedding space is robust and highly structured. Our models allow us to predict diverse outcomes ranging from early mortality to personality nuances, outperforming state-of-the-art models by a wide margin. Using methods for interpreting deep learning models, we probe the algorithm to understand the factors that enable our predictions. Our framework allows researchers to discover potential mechanisms that impact life outcomes as well as the associated possibilities for personalized interventions.Bio: Sune is a Professor of Networks and Complexity Science at DTU Compute, Technical University of Denmark. I’m also a Professor of Social Data Science at the Center for Social Data Science (SODAS), University of Copenhagen. His work focuses on quantitative understanding of social systems based on massive data sets. A physicist by training, my research draws on approaches from the physics of complex systems, machine learning, and statistical analysis. I work on large-scale behavioral data and while my primary focus is on modeling complex networks, my research has made substantial contributions on topics such as human mobility, sleep, academic performance, complex contagion, epidemic spreading, and behavior on twitter.
Carolina Mattsson on The circulation of digital money Network Science Institute 2024-09-03 | Date: 06/25/24Abstract: Circulation is the characteristic feature of successful currency systems, from community currencies to cryptocurrencies to national currencies. This talk will present a network approach to studying the circulation of money within such systems, touching on the data, the theory, and the tools. Modern payment infrastructure keeps digital transaction records that capture an ever greater share of circulation. A theory of walk processes on networks gives us a solid basis for representing such data, and lets us develop highly effective network analysis tools. We will discuss applied analyses of Sarafu, a digital community currency active in Kenya, and of a mobile money system elsewhere in Africa operating in the national currency. Several specific findings have concrete implications for humanitarian and development policy. More broadly, the ability to study the circulation of digital money in detail stands to accelerate our understanding of payment systems, the currency systems they comprise, the financial systems they underpin, and the economic systems they enable.Bio: Dr. Carolina Mattsson is a network scientist developing analysis tools and modelling frameworks for studying the economy as a complex system. She is a Researcher at CENTAI Institute doing work on production networks, payment systems, and temporal networks. Aspects of her research are explicitly policy- or industry- facing, having participated in projects with the Dutch Ministry of Economics Affairs, Statistics Netherlands, Telenor Research, IFC (World Bank), ING, and Intesa Sanpaolo. Carolina has a PhD in Network Science from the Network Science Institute at Northeastern University. During her PhD, she was supported by the NSF Graduate Research Fellowship Program and as a member of the Lazer Lab. Before joining CENTAI, Carolina was postdoctoral researcher in the Computational Network Science group at Leiden University.
Oren Tsur on Context Dependent: Multimodal Architectures for Social Groundings of Language Models Network Science Institute 2024-09-03 | Date: 05/31/24Abstract: State-of-the-Art Natural Language Processing (NLP) systems are trained on massive collections of data. Traditionally, NLP models are uni-modal: one form of data, e.g., textual data, is used for training. However, recent trends focus on multimodality, utilizing multiple forms of data in order to improve the system’s performance on classic tasks as well as broadening the capabilities of AI systems. Image and code are the two common modalities that are used in training popular tools such as OpenAI’s GPT and Google's Gemini, among other LLMs.. Language, however, is not merely a collection of stand-alone texts, nor texts merely grounded in image or aligned with code. Language is primarily used for communication between speakers in some social settings. The meaning (semantic, pragmatic) of a specific utterance is best understood by interlocutors that share some common ground and are aware of the context in which the communication takes place. In this talk I will demonstrate the the benefits of the multi-modal framework through three unique tasks: conversational stance detection, the detection of hate mongers, and through modeling distributed large-scale coordinated campaigns.Bio: Dr. Oren Tsur is an Assistant Professor (Senior Lecturer) at the Department of Software and Information Systems Engineering at Ben Gurion University in Israel where he heads the NLP and Social dynamics Lab (NASLAB), and serve as the director of the newly founded Interdisciplinary Center for the Study of Digital Politics and Strategy (DPS@BGU). Oren’s work combines Machine Learning, Natural Language Processing (NLP), Social Dynamics, and Complex Networks. Specifically, Oren’s work varies from sentiment analysis to modeling speakers’ language preferences, hates-speech detection, community dynamics, and adversarial influence campaigns. Oren serves as an editor and Senior Program Committee member in venues like ACL, EMNLP, WSDM and ICWSM and as a reviewer for journals ranging from TACL to PNAS and Nature. Oren’s work was published in top NLP and Web Science venues. His work/s on sarcasm detection was listed in the “top 50 inventions of the year” in Time Magazine’s Special technology issue. Academic homepage: https://www.naslab.ise.bgu.ac.il/orentsur
Sandra González-Bailón on Networks as Maps of the Information Environment Network Science Institute 2024-09-03 | Date: 03/29/24Series: Spring Complexity SeriesAbstract: A lot of recent research pays attention to the psychological and cognitive factors that explain engagement with political information (including misleading content). This work offers important insights that help design interventions at the level of individuals. However, more systemic approaches are also needed to capture the aggregate characteristics of the information environment individuals navigate – and help create. In this talk, I will discuss recent research that uses networks to map information environments based on exposure behaviors. These networks help us identify pockets of problematic content and the types of audiences more likely to engage with that material. They also help us compare (and differentiate) modes of exposure and the different layers that structure the current media environment.
John Palowitch on Fake Graphs, Real Results: Learning with Synthetic Graphs Network Science Institute 2024-09-03 | Date: 03/25/24Abstract: This talk will cover recent work related to training and evaluating graph ML models on synthetic graphs. First, we discuss GraphWorld, a framework and package for generating a high-diversity set of medium-scale graphs for finding edge-cases of GNN performance. Second, we discuss new graph generative models that have been added to GraphWorld since its release. Finally, we discuss more recent work on generating large, individual synthetic graphs, and the challenges involved in training a GNN model on such graphs.Bio: John Palowitch is a Research Scientist in Google Research based in San Francisco, CA working at the intersection of graph machine learning and LLMs.
Vincent Thibeault on The low-rank hypothesis of complex systems Network Science Institute 2024-09-03 | Date: 01/31/24 Abstract: Behind the blur caused by the high-dimensional nonlinear dynamics and the intricate organization of complex systems, hide essential mechanisms that explain the emergence of macroscopic phenomena. To uncover those mechanisms, it has been common practice for researchers to model complex systems using dynamics that depend upon low-rank matrices describing the networks of interactions---what we call the low-rank hypothesis. We present three indicators of the low-rank hypothesis and evidence of its ubiquity among random network models used in various fields of study, ranging from network science and machine learning to neuroscience. We then verify the hypothesis for real networks of various origins and use our observations to examine the repercussions of the low-rank hypothesis on nonlinear dynamics. In particular, we show that having networks described by low (effective) rank matrices enables the dimension reduction of the nonlinear dynamics they support. As a surprise, we find that higher-order interactions emerge naturally from an optimal dimension reduction, which demonstrates the profound interplay between the description dimension of a complex system and the possibility of having higher-order interactions.Bio: Vincent Thibeault is a Ph.D. candidate in Physics at Université Laval in Québec City, co-advised by Antoine Allard and Patrick Desrosiers. His main research activities concern dynamical processes on networks and the optimal compression of their mathematical descriptions, with applications ranging from computational neuroscience to epidemic spreading. His latest publication, featured in Nature Physics, delves into the fundamental question of the low-dimensional representation of complex systems. Additionally, Vincent’s interests and research in complexity science extend to other areas, including synchronization, spectral graph theory, adaptation, and information theory.
COVID-19 Misinformation Roundtable: the state of the research Network Science Institute 2022-04-13 | This roundtable on April 8, 2022 featured leading researchers on misinformation regarding COVID-19, as well as remarks from special guest, the US Surgeon General, Dr. Vivek Murthy. It was cosponsored by Northeastern's NULab for Texts, Maps, and Networks and Network Science Institute, the COVID States Project, the Social Science Research Council, the Rockefeller Foundation, and the Digital Public Library of America.FEATURING SPECIAL GUESTVice Admiral Vivek H. Murthy, MD, MBA U.S. Surgeon General, Department of Health and Human ServicesPANELISTS Dolores Albarracin ( University of Pennsylvania)Leticia Bode (Georgetown University)Filippo Menczer ( Indiana University) Brendan Nyhan ( Dartmouth College) Katherine Ognyanova ( Rutgers University) David Rand ( MIT) FACILITATORSMatthew Baum (Harvard University)David Lazer ( Northeastern University)
Network Medicine (Havana Parody) Network Science Institute 2022-01-13 | Created by: Patrick SayersAyan ChatterjeeSamuel WestbyUla Widocki
Brennan Klein Constructing, comparing, and reconstructing networks Network Science Institute 2020-11-24 | Complex networks are the syntax of complex systems; they are models that allow us to study phenomena across nature and society. And because they are models, the famous “all models are wrong, but some are useful” quotation rings especially true. We need to use the right networks to properly study complex systems, and in order to do so, the methods we use to create and analyze networks must be fit for purpose. This motivation has guided much of my dissertation, and in it, I explore three related themes around constructing, comparing, and reconstructing complex networks.In the first chapter, I describe a theoretical and computational infrastructure that allows us to ask whether a given network captures the most informative scale to model the dynamics in the system. We see that many real world networks (especially heterogeneous networks) exhibit an information holarchy whereby a coarse-grained, macroscale representation of the network has more effective information than the original microscale network. In the next chapter, I consider the challenging problem of comparing pairs of networks and quantifying their differences. These tools are broadly referred to as “graph distance” measures, and there are dozens used throughout Network Science. However, unlike in other domains of Network Science where rigorous benchmarks have been established to compare our surplus of tools, there is still no theoretically-grounded benchmark for characterizing these tools. To address this, I propose that simple, well-understood ensembles of random networks are natural benchmarks for network comparison methods. In this chapter, I characterize over 20 different graph distance measures, and I show how this simple within-ensemble graph distance can lead to the development of new tools for studying complex networks. The final chapter is an example of exactly that: I show how the within-ensemble graph distance can be used to characterize and evaluate different techniques for reconstructing networks from time series data. Tying together the original theme of using the “right” network, this chapter addresses one of the most fundamental challenges in Network Science: how to study networks when the network structure is not known. Whether it’s reconstructing the network of neurons from time series of their activity, or identifying whether one stock’s price fluctuations cause changes in another’s, this problem is ubiquitous when studying complex systems; not only that, there are (again) dozens of techniques for transforming time series data into a network. In this chapter, I measure the within-ensemble graph distance between pairs of networks that have been reconstructed from time series data using a given reconstruction technique. What I find is that different reconstruction techniques have characteristic distributions of distances and that certain techniques are either redundant or underspecified given other more comprehensive methods. Ultimately, the goal of this dissertation is to stress the importance of rigorous standards for the suite of tools we have in Network Science, which ultimately becomes an argument about how to make Network Science more useful as a science.
Taha Yasseri Fooling with Facts! Quantifying cognitive biases with large-scale online experiments Network Science Institute 2020-07-30 | Humans’ cognitive abilities are subject to various biases both at the individual and collective levels. Such biases can have dramatic effects on our decision making. The rise of social media, crowd-sourced information repositories, and user-based information channels have raised the concern that “fake [or biased] information” along with “filter bubbles” will further distort the perception of reality that online users gain through online media leading to ill-informed decisions in political events and beyond. The proposed research aims to study and quantify two main processes in which such biases can be induced and affect our judgments, through a series of large scale field experiments. In particular we will focus on anchoring effect, that is irrational reliance on the provided numerical information, even unrelated, when making decision.
Brent Reidy The Impossibility of Knowing Everything Network Science Institute 2020-03-04 | As the world of knowledge shifts from paper to petabytes, institutions like the New York Public Library are increasingly able to make vast amounts of information accessible to a global audience of researchers and learners. This is a moment of both triumph and anxiety. The scale of available information may outstrip the human capacity to assess, understand, and generate knowledge. Increasingly, it is recognized that machines have a fundamental role to play, not only in helping to organize, unlock, and sift through the information collected, but also in finding and drawing new connections among data. The role of the Library is thus evolving: our task is not just to acquire and preserve information, but to make explicit choices about how information is made available to both man and machine in order to achieve our mission of advancing knowledge.
Daniel Aldrich How Social Ties Matter in Crisis Network Science Institute 2019-06-18 | The 11 March 2011 Great East Japan Earthquake and associated tsunami and nuclear meltdowns took nearly 20,000 lives, created half a million refugees, and affected energy policies as far away as Germany, Switzerland, and Italy. In Japan, mortality rates in coastal communities in the Tohoku region were not constant; instead, they varied widely from town to town. During the recovery period, some towns have rebuilt damaged infrastructure, reopened schools, and repopulated downtowns. Others have not been at successful. What explains the variation in mortality during the tsunami and recovery after the disaster? Using data gathered from fieldwork, interviews, and surveys, Aldrich looks closely at the role of social capital and networks to provide concrete suggestions for ways to help us survive and thrive in disaster. While many of us envision disaster mitigation in terms of physical infrastructure, the findings instead point to the power of social infrastructure to reduce mortality and accelerate post disaster recovery.
Phil Chodrow Configuration Models of Random Hypergraphs and their Applications Network Science Institute 2019-04-01 | Networks of dyadic relationships between entities have emerged as a dominant paradigm for modeling complex systems. Many empirical "networks”—such as collaboration networks; co-occurence networks; and communication networks—are intrinsically polyadic, with multiple entities interacting simultaneously. Historically, such polyadic data has been represented dyadically via a standard projection operation. While convenient, this projection often has unintended and uncontrolled impact on downstream analysis, especially null hypothesis-testing. In this work, we develop a class of random null models for polyadic data in the framework of hypergraphs, therefore circumventing the need for projection. The null models we define are uniform on the space of hypergraphs sharing common degree and edge dimension sequences, and thus provide direct generalizations of the classical configuration model of network science. We also derive Metropolis-Hastings algorithms in order to sample from these spaces. We then apply the model to study two classical network topics—clustering and assortativity—as well as one contemporary, polyadic topic—simplicial closure. In each application, we emphasize the importance of randomizing over hypergraph space rather than projected graph space, showing that this choice can dramatically alter directional study conclusions and statistical findings. For example, we find that many of social networks we study are less clustered than would be expected at random, a finding in tension with much conventional wisdom within network science. Our findings underscore the importance of carefully choosing appropriate null spaces for polyadic relational data, and demonstrate the utility of random hypergraphs in many study contexts. Link to arXiv paper: [arxiv.org/abs/1902.09302]
Mark Newman Patterns and surprises in rich but noisy network data Network Science Institute 2019-04-01 | In most empirical studies of networks, it is assumed that the data we collect accurately reflect the true structure of the network, but in practice this is rarely true: most network data are noisy, containing measurement error, false positives, false negatives, contradictory observations, or missing data. On the other hand the data can also be richly structured, with measurements of different types, repeated observations, annotations, or metadata. This talk will address the problem of making best estimates of network structure from such rich but noisy data, with a variety of example applications in social and biological networks. In the process, we will see that the pattern of errors in network data is far from random and can teach us some intriguing lessons not only about the data but also about the underlying systems they describe.
Erdem Sariyüce Hierarchical Dense Subgraph Discovery: Models, Algorithms, Applications Network Science Institute 2019-03-20 | Finding dense substructures in a network is a fundamental graph mining operation, with applications in bioinformatics, social networks, and visualization to name a few. Yet most standard formulations of this problem (like clique, quasi-clique, densest at-least-k subgraph) are NP-hard. Furthermore, the goal is rarely to find the “true optimum” but to identify many (if not all) dense substructures, understand their distribution in the graph, and ideally determine relationships among them. In this talk, I will talk about a framework that we designed to find dense regions of the graph with hierarchical relations. Our model can summarize the graph as a tree of subgraphs. With the right parameters, our framework generalizes two widely accepted dense subgraph models; k-core and k-truss decompositions. We present practical sequential and parallel local algorithms for our framework and empirically evaluate their behavior in a variety of real graphs. Furthermore, we adapt our framework for bipartite graphs which are used to model group relationships such as author-paper, word-document, and user-product data. We demonstrate how proposed algorithms can be utilized for the analysis of a citation network among physics papers and user-product network of the Amazon Kindle books.
Rose Yu Learning from Large-Scale Spatiotemporal Data Network Science Institute 2019-03-04 | In many real-world applications, such as climate science, intelligent transportation, sports analytics, and physics, machine learning is applied to large-scale spatiotemporal data. Such data is often nonlinear,high-dimensional, and demonstrates complex spatial and temporal correlation. Deep learning provides a powerful framework for feature extraction, but existing models are still insufficient to handle the complex structures in spatiotemporal data.In this talk, I will show how to design deep learning models to learn from large-scale spatiotemporal data, especially for dealing with non-Euclidean geometry, long-term dependencies and incorporating logical/physical constraints. I will showcase the application of these models to a variety of problems in transportation, sports, circuit design, and aerospace control. I will also discuss the opportunities and challenges of applying deep learning to large-scale spatiotemporal data.
Elena Naumova Using Fractals to Measure Immunosenescence Network Science Institute 2019-01-25 | A scale-free behavior has been observed in many living systems at micro- and macro-scales. These systems continue to stimulate the interests in theoretical studies, including the understanding of aging and specifically of immunosenescence. The immune system represents the major system with a large cellular component dedicated to the generation of adaptive memory to pathogens. It is this component of immunity which is the most instructive in understanding the life stages of humans.In the experimental studies of the adaptive immune system, we had observed a scale-free network governing the repertoire of memory T-cells (Naumov et al, 2003). At the molecular level, we observe that a memory immune response to influenza virus becomes diverse upon repeated exposures to the virus that can be modeled as a fractal self-similar system. Theoretical explanation of experimental findings has been described by the small-world construction (Ruskin and Burns, 2006) as a special case of the scale-free network (Albert and Barabasi, 2002). We then simulated the fractal behavior mimicking immune memory - its generation, maintenance and senescence (Naumova et al, 2008) and experimentally illustrated the general stability of the power-law structures and age-related changes. Our recent theoretical work confirms the assumptions that multiple expansion-contraction cycles define the robustness of immune response and correspond to memory formation (Saito and Narikiyo, 2011). Saito and Narikiyo had proposed the dynamical network of the adaptive immune system as a self-organized critical state in which the avalanche feedback reinforcement may reduce immunosenescence.At the population level, we also observed the evidence of exposure to influenza as a marker of “immunological age.” In the cohort of healthy donors, each encounter with an infectious agent was unique for every person. Yet, the commonality in responses formed “immunological kinship” among all affected individuals, manifested by a preserved T-cell clonal pool. The diverse responses to flu and changes in diversity allow us to make an inference to “immunological kinship” and “immunological age.” Our experimental data indicate that at a certain point the continuing exposures to influenza begin to decrease the diversity of immune response. These observations lead us to explore theoretical conditions governing the “stable” and “volatile” components of the T-cell repertoires via dynamic neural networks. Such separation allowed us to detect a condition indicative of acceleration of immune aging. We derived the initial network parameters based on a specially designed anchored power-law regression fit of experimental data from middle-aged and older donors over time and illustrated age acceleration and immunosenescence in humans.
Jure Leskovec Deep Learning on Graphs Network Science Institute 2019-01-22 | Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks. We will discuss applications to web-scale recommender systems, healthcare and knowledge representation and reasoning.
Bruno Lepri The urban dataclysm: Understanding and rewiring cities Network Science Institute 2019-01-09 | In the last decades, cities have been largely acknowledged as complex and emergent systems as opposed to top-down planned entities. Thus, a new city science is emerging that aims at an empirical analysis of urbanization processes. However, it is evident the lack of understanding of the dynamics that regulate people interactions, their relationship with urban characteristics, and their influence on socio-economic outcomes of cities. Nowadays, massive streams of human behavioural data and urban data combined with increased analytical capabilities are creating unprecedented possibilities for understanding global patterns of human behaviour and for helping researchers to better understand relevant problems for cities and also whole societies. For example, analysing the digital traces people leave every day (e.g., mobile phones and social media data, credit card transactions, etc.) researchers were able, among the other things, to estimate the socio-economic status of territories, to monitor the vitality of urban areas and to predict neighbourhood’s crime levels.In my talk, I describe some recent works where we have leveraged data from public andcommercial entities in order (i) to infer how vital and liveable a city is, (ii) to find the urban conditions (e.g., mixed land use, mobility routines, safety perception levels, etc.) that magnify and influence urban life, and (iii) to study their relationship with societal outcomes such as criminality and urban segregation. Our results open the door for a new research framework to study and to understand cities, and societies, by means of computational tools (i.e. machine learning approaches) and novel sources of data able to describe human life with an unprecedented breath, scale and depth.
Benjamin Gyori Modeling complex biological systems with automated assembly Network Science Institute 2019-01-09 | Building computational models of complex systems, including ones governing the behavior of biological cells, is a laborious process involving manual information gathering and model implementation. This makes it difficult to build models based on known causal mechanisms, creating a gap between the scope of typical models, and the scale of data they are meant to help interpret. We developed INDRA (the Integrated Network and Dynamical Reasoning Assembler), a system for automatically assembling mechanistic models directly from English language, including from the scientific literature. INDRA interfaces with natural language processing systems to extract descriptions of mechanisms from text, and uses knowledge assembly algorithms to fix certain errors, resolve redundant knowledge, infer missing information, and assess belief. INDRA then uses this knowledge base to produce models in different formalisms, including rule-based dynamical models, and causal graphs. We present applications of INDRA including (i) a model automatically built from an English language description of mechanisms which can reproduce resistance to a melanoma cancer drug (ii) a model of molecular signaling pathways built by machine-reading ~95,000 scientific publications, which can provide mechanistic explanations to drug perturbations in a cancer cell line (iii) a human-machine dialogue system in which a user can gather information and build a mechanistic hypothesis for an observed phenomenon by talking with a computer partner.
Martin Rohden Self-Organization of Dragon King Failures Network Science Institute 2018-10-01 | Extreme events and critical transitions occur in a variety of natural and artificial systems. The most crucial task for understanding and explaining extreme events is usually the question of predicting these events. The notion of self-organized criticality is often used to explain these events. However, here the underlying mechanisms of extreme events are the same as for all other events which makes prediction difficult. Recently the novel concept of Dragon Kings was introduced, where extreme events have a distinct mechanism from all other events. Here we introduce a simple network model where nodes self-organize to be either weakly or strongly protected against failure in a manner that captures the trade-off between degradation and reinforcement of nodes inherent in many network systems. If strong nodes cannot fail, any failure is contained to a single, isolated cluster of weak nodes and the model produces power-law distributions of failure sizes. We classify the large, rare events that involve the failure of only a single cluster as “Black Swans.” In contrast, if strong nodes fail once a sufficient fraction of their neighbors fail, then failure can cascade across multiple clusters of weak nodes. If over 99.9% of the nodes fail due to this cluster hopping mechanism, we classify this as a “Dragon King,” which are massive failures caused by mechanisms distinct from smaller failures. We find that once an initial cluster of failing weak nodes is above a critical size, the Dragon King mechanism kicks in, leading to piggybacking system-wide failures. We demonstrate that the size of the initial failed weak cluster predicts the likelihood of a Dragon King event with high accuracy and we develop a simple control strategy that can dramatically reduce Dragon Kings and other large failures.
Abigail Horn Modeling Food Supply Network Structure to Identify Outbreak Origins Network Science Institute 2018-10-01 | During a large-scale outbreak of foodborne disease, rapidly identifying the source, including both the food vector carrying the contamination and the location source in the supply chain, is essential to minimizing the impact on public health and industry. However, tracing an outbreak to its origin is a challenging problem due to the complexity of the food supply system and the absence of publicly available data on the ne-grained structure of the intra-company supply chain.In this talk I will present a framework we have developed that integrates novel modeling techniques with nontraditional data sources to identify the source of emerging outbreaks of foodborne disease. Approaching this problem requires (i) modeling the network structure of the aggregated food supply system and (ii) developing network-theoretic methods to solve the food vector and contamination location source identication problems. I will discuss our approach to both parts of this problem, experiences implementing these methods at Germany's federal-level food regulatory agency, and a developing project to extend this work to the US context.First, I will introduce our approach to model the network structure of the aggregated food supply system utilizing publicly available statistical data and methods from transport demand modeling [1]. Then I will review our network epidemiological approach to identify the food and location source of an outbreak given the food supply network model and reported locations of illness [2,3]. To solve the source location problem we formulate a probabilistic model of the contamination diusion process and derive the maximum likelihood estimator for the source location. We use the location source estimator as the basis of an information theoretic approach to identify the food vector source carrying the contamination. A statistical test is developed to identify the food item network that best ts the observed distribution of illness data.Case studies in on several recent outbreaks in Germany suggest that the application of the combined network models and inference methods could have substantial benets for investigators during the onset of outbreaks of foodborne disease. Beyond foodborne disease, we are applying these methods to identify the source of spread in network-based diusion processes more generally, including disease spread through global transport networks and bacterial contaminations spread through water distribution networks.
Jaan Altosaar food2vec: Augmented Cooking with Machine Intelligence Network Science Institute 2018-08-20 | jaan.io/food2vec-augmented-cooking-machine-intelligenceEmbedding models are used in production for Google Search, in the Discover Weekly recommendation system at Spotify, and for learning representations of biological systems like genes and proteins. In this work, we develop an embedding model for foods based on patterns in a large recipe dataset. A recommendation system for food is built based on the embedding model, and we show that our model learns concepts such as which foods are complementary or which foods can be substituted for each other in recipes. The code and data are open source and readily extendable to new kinds of data.
Federico Levi Inside Nature Physics Network Science Institute 2018-08-17 | Since its launch in 1869, Nature has seen its mission as two-fold: facilitating the prompt communication of the most important scientific developments to the relevant research communities, while at the same time fostering a greater appreciation of these great works of science amongst the wider public. Although the publishing landscape for scientific research is currently undergoing a period of rapid change, these core principles remain largely unchanged. In this talk, I will endeavour to explain how Nature editors -- in particular those based at Nature Physics apply these principles in practice, and so determine which few of the many excellent research submissions that we receive make it through to publication.
Walter Quattrociocchi From Confirmation Bias to Echo Chambers on Facebook Network Science Institute 2018-07-25 | Social media are pervaded by unsubstantiated or untruthful rumors, that contribute to the alarming phenomenon of misinformation. The widespread presence of a heterogeneous mass of information sources may affect the mechanisms behind the formation of public opinion. Such a scenario is a florid environment for digital wildfires when combined with functional illiteracy, information overload, and confirmation bias. In this essay, we focus on a collection of works aiming at providing quantitative evidence about the cognitive determinants behind misinformation and rumor spreading. We account for users’ behavior with respect to two distinct narratives: a) conspiracy and b) scientific information sources. In particular, we analyze Facebook data on a time span of five years in both the Italian and the US context, and measure users’ response to i) information consistent with one’s narrative, ii) troll contents, and iii) dissenting information e.g., debunking attempts. Our findings suggest that users tend to a) join polarized communities sharing a common narrative (echo chambers), b) acquire information confirming their beliefs (confirmation bias) even if containing false claims, and c) ignore dissenting information.
M. Carmen Miguel Lopez Exploring the Effects of Social Influence on Flocking Dynamics Network Science Institute 2018-07-09 | Co-authors: Javier Cristín, Jack T. Parley, and Romualdo Pastor-SatorrasSocial relationships characterize the interactions that occur within social species and may have an important impact on collective animal motion. Here, we consider some variations of the standard Vicsek model for collective motion to incorporate social influence. The main assumption of the Vicsek and other similar models of collective motion is that particles tend to orient their velocity parallel to the average velocity in a local neighborhood, independently of their identity, leaving aside the fact that real interactions between moving animals can be more intricate. By incorporating interactions mediated by an empirically motivated scale-free topology that represents a heterogeneous pattern of social contacts, we observe that the degree of order of the model is strongly affected by network heterogeneity: more heterogeneous networks show a more resilient ordered state; while less heterogeneity leads to a more fragile ordered state that can be destroyed by sufficient external noise. Another important aspect of collective animal motion is the existence of behavioral changes at the individual level, which may by transmitted to the group, triggering intermittent collective rearrangements or even phase transitions at the macroscopic level. We examine avalanching behavior in the collective motion of flocks where a single individual has a long range orientational contagion effect over the rest of individuals. We observe that the response of the flock to changes in the direction of motion of such individuals shows an intermittent avalanche-like behavior, characterized by sudden reorientations of the trajectories of groups of individuals. We show that the distribution of avalanche sizes and durations show scale-free signatures in analogy with self-organized critical processes. The results obtained appear to be in fairly good agreement with recent experimental results characterizing collective evasion in schooling fish. Yet, more empirical data are needed to obtain a better understanding of the patterns of collective rearrangements in other flocking systems, where individual differences and/or social interactions may have an important effect.
Aida Nematzadeh Language Learning and Representation in Humans and Computers Network Science Institute 2018-07-05 | Language is one of the greatest puzzles of both human and artificial intelligence (AI). Language learning happens effortlessly in children; yet, it is a complex process that we do not fully understand. Moreover, although access to more data and computation has resulted in recent advances in AI systems, they are still far from human performance in many language tasks. How do humans learn and represent language? And how can this inform AI?In this talk, I focus on representation of semantic knowledge -- word meanings and their relations -- which is an important aspect of child language learning and AI systems: it impacts how word meanings are stored in, searched for, and retrieved from memory. First, I talk about how humans learn and represent semantic knowledge. I show that, using the evolving knowledge of word relations and their contexts, we can grow a network that exhibits the properties of adult semantic knowledge. Moreover, this can be achieved using limited computation. Next, I explain how investigating human semantic processing helps us model semantic representations more accurately. I show that recent neural models of semantics, despite being trained on huge amount of data, fail at capturing important aspects of human similarity judgements. I also show that a probabilistic topic model does not have these problems, suggesting that exploring different representations may be necessary to capture different aspects of human semantic processing.
Sam Scarpino Network heterogeneity induces entropy barriers in social contagion Network Science Institute 2018-06-20 | Infectious disease outbreaks and other contagion, e.g., memes, recapitulate biology: they emerge from the multi-level interaction of hosts, pathogens, and their shared environment. Here, by studying the predictability of a diverse collection of historical outbreaks--including, chlamydia, gonorrhea, hepatitis A, influenza, dengue, measles, polio, whooping cough, Ebola, and Zika--we identified a fundamental entropy barrier for contagions. We further demonstrate that social network heterogeneity is the most likely explanation for observed differences in entropy across contagions. Our results highlight the importance of dynamic modeling approaches for prediction and hypothesis testing.
Antoine Allard Towards an effective structure of complex networks and its contribution to... Network Science Institute 2018-06-20 | Complex networks offer a powerful paradigm to study the structure of complex systems on a common basis, using the same concepts and nomenclature to represent systems as various as human interactions, ecosystems, the brain or the Internet. Although this impressive feature suggests some sort of universality, the available toolbox to characterize these structures is incomplete since it does not provide a thorough understanding of their organization. Indeed, many key properties have been identified as fundamental structural features, but they only offer a partial glimpse at the global picture and we are still unable to accurately predict the evolution of many dynamical processes taking place on real networks. In other words, we still lack a comprehensive and intensive way to capture and synthesize the full essence of these structures.In this presentation, I will discuss two promising approaches synthesizing the macroscopic organization of real complex networks into a set of local properties, which in turn naturally define random graph ensembles reproducing the said macroscopic features based on local connection rules only. I will then discuss how the various tools developed to unveil this effective structure of networks can be used to shed light on new phenomena in Epidemiology and Neuroscience. This will be illustrated via ongoing projects dealing with the current threat of a Zika epidemic and the organization of the connectome across species.
Aaron Clauset Researcher productivity and data-driven predictions in the science of science Network Science Institute 2018-06-20 | The desire to predict discoveries, to have some idea, in advance, of what will be discovered, by whom, when, and where, pervades nearly all aspects of modern science, from individual scientists to publishers, from funding agencies to hiring committees. In this talk, I will present a simple conceptual framework for thinking broadly about prediction in the context of the "science of science." Then, I will dive deep into results on the predictability of researcher productivity over an entire career. Over the past 60 years, research on this subject has repeatedly validated a canonical productivity pattern across fields, in which average productivity exhibits a rapid rise to an early peak followed by a long and gradual decline. Using comprehensive data on publication and employment histories for an entire academic field, we show that this pattern holds only for a minority of individual researchers, and that instead individual productivity profiles are highly variable, although some features do correlate with certain covariates. We close with a forward-looking perspective on the implication of these results for the broader goals of the science of science, and describe a set of opportunities and risks for the science of science in the age of big data. This is joint work with Samuel F. Way, Allison C. Morgan, Roberta Sinatra and Daniel B. Larremore.
Aaron Clauset The trouble with community detection Network Science Institute 2018-06-19 | A common task in network analysis is to seek a coarse-graining of the network into modules or communities, which describe the large-scale architecture of the network. For instance, we might want to find social groups within a network of friendships, functional modules among gene regulatory interactions, or compartments within food webs. However, different algorithms will return different communities for the same network, and this presents a conundrum for scientific interpretation: which set of communities are the real ones? In this talk, I'll show how using node attributes or "metadata" can solve this problem, by guiding the community detection process toward useful outcomes. The resulting algorithm, which is a generalization of the powerful stochastic block model, is more accurate than any algorithm that uses only network structure or node metadata alone, and can automatically learn the underlying correlation between metadata and structure, if one exists. To illustrate these features, I'll show results for applying the method both to synthetic networks with known structure and to real-world networks with unknown structure. I'll close with a few general comments about the recently proved No Free Lunch theorem in community detection, and the utility of community detection methods in scientific applications. This is joint work with Mark Newman.
Cody Dunne Interactive network visualizations for reasoning, communication, and collaboration Network Science Institute 2018-06-15 | The modern world is awash in complex data that can contain the keys to improving our lives. The scope of this data has rapidly outpaced our capabilities to analyze and comprehend, so we turn to computers to help. However, state-of-the-art technology can only supplement the human element. People assist in each stage of data science, whether it's data cleaning, understanding algorithm design, exploring computed results, or collaborating and sharing for decision-making. To present complex information to humans, we use visualizations that leverage our extraordinary perceptual system which can detect trends, clusters, gaps, and outliers almost instantly. A challenging and increasingly important type of data is networks of entities and their relationships. Networks have been are widely used across diverse disciplines to reason about complex behavior. These analyses involve understanding relationships, as well as associated attributes, statistics, or groupings. The omnipresent node-link visualization excels at showing topology and features simultaneously, but many are difficult to extract meaning from due to poor layout or shoehorning inherent complexity into limited space. The first part of my talk will detail techniques for measuring the readability of node-link visualizations and strategies to help users create more effective and understandable visualizations.Moreover, analyses of complex data often requires several sessions, and when returning later it can be difficult to recall the steps in your workflow. Data science in many domains is also highly collaborative. Multiple analysts may be working alongside stakeholders with varying expertise and time constraints. The second part of my talk addresses these needs, and I introduce visualization strategies that assist in making analysis workflows repeatable, free of errors, understandable, and easily shareable.
Sandra González-Bailón Networks and the Unintended Consequences of Communication Network Science Institute 2018-06-14 | Why does human action trigger unintended effects? This question has intrigued social scientists for decades – and only now, with the help of network science, have we began to unravel the forces that set those chain reactions in motion. The unintended consequences of human action take many forms and are captured by many metaphors: from self-fulfilling prophecies to cumulative effects; from negative feedback loops to virtuous circles. Communication networks pulsate at the heart of all these processes of change. Digital technologies, with their trove of data and tools, allow us to tap into those networks, and solve the puzzle of why our actions generate outcomes that we did not intend or envision. This talk will offer an account of the progress made in recent decades, and dive into the details of my own research, which aims to illuminate how networks facilitate large-scale coordination -- and dynamics of change that lay beyond the control of any one individual involved.
Danielle Bassett The Network Architecture of Human Thought Network Science Institute 2018-06-12 | Human thought is predicated on a complex architecture of inter connections that enable information transmission between distinct areas of the brain. Yet gaining a fundamental understanding of this architecture has remained challenging, largely due to insufficiencies in traditional imaging techniques and analytical tools. In concerted efforts to address these challenges, neuroscientists have begun to combine recent breakthroughs in non-invasive brain imaging techniques with the conceptual notions and mathematical tools of network science– leading to the emerging field of network neuroscience. I will highlight early successes in this field leading to fundamental understanding of healthy human thought, its development over childhood, and its alteration in psychiatric disease and neurological disorders. I will close by commenting on current frontiers and future potential in health care, business, and education sectors.
Howard Sesso Understanding the food matrix through large-scale clinical trials and... Network Science Institute 2018-06-12 | Numerous well-conducted epidemiologic studies have identified promising nutrients, foods, and dietary patterns as a means of characterizing the food matrix for its potential role in the prevention of chronic disease and key mechanistic pathways incorporating biomarkers, genetics, and other omics. Yet large-scale epidemiologic studies have inherent methodologic limitations in terms of study design, participant characteristics, confounding, and interactions with other nutritional components that render any assertion of causality – including for commonly used dietary supplements – problematic. Our research has also sought to understand the role of perceived “superfoods” on health outcomes, generating questions on the competing roles of the food versus its key nutrients as part of the food matrix. In the US, dietary supplement use remains highly prevalent, diverse, and without equivalent federal regulations and testing as required for drugs. As a result, a large segment of the population takes dietary supplements without any knowledge of its short- and long-term effects, complicating our interpretation of the food matrix. Randomized clinical trials (RCTs) of dietary supplements remain the gold standard to test whether nutrient(s) have purported health benefits. Examples of perceived “failures” for RCTs of dietary supplements and chronic disease have led many to question the role of vitamin and mineral supplements for disease prevention and their place in the food matrix. However, RCTs have a unique ability to isolate specific components of the food matrix to potentially understand how nutrients, foods, and dietary patterns interact. RCTs conducted by our Division have tested whether dietary supplements containing promising bioactives and nutrients impact health outcomes, including the VITamin D and OmegA-3 TriaL (VITAL), Physicians’ Health Study II (PHS II), and the COcoa Supplement and Multivitamin Outcomes Study (COSMOS), among others. We will discuss the importance of these RCTs in assessing the role of dietary supplements in the context of the food matrix and health outcomes, with the goal of improving our understanding of how diet affects chronic disease risk.
Vipul Chitalia ‘Foodome’ to thrombolome: A link to the CVDs in CKD patients Network Science Institute 2018-05-23 | Chronic kidney disease (uremia) has reached pandemic proportion, where more than 10% of world’s population is affected by it. Cardiovascular diseases constitute the major cause of morbidity and mortality in CKD patients, which is primarily driven by uremic solutes, unique CKD-specific risk factors. Emerging evidence points to the importance of uremic solutes in inducing hyperthrombotic phenotype across the entire spectrum of CKD. A specific group of uremic solutes generated in gut retained with renal impairment and inflicting thrombosis (gut-kidney-vascular axis) is recently termed as ‘thrombolome’. They activate specific xenobiotic pathway to upregulate highly procoagulant protein termed tissue factor (TF). Validation of this uremia-specific pathway in two large patient cohorts provides human relevance and demonstrates it as a quantifiable risk factor. Importantly, that it can be potentially modulated by food makes it a modifiable cardiovascular risk factor. Deciphering the influence of food (foodome) on the thrombolome will have deep and broad implications in patients with CKD.
Shenghua Liu HoloScope: Topology-and-Spike Aware Fraud Detection on Big Graphs Network Science Institute 2018-05-21 | As people are spending a lot time on Youtube, Facebook, and Twitter, and becomming reliable on reviews from Amazon and Yelp, online fraud has become a serious problem due to the high profit it offers to fraudsters. Since online fraudsters invest more resources, including purchasing large pools of fake user accounts and dedicated IPs, fraudulent attacks become less obvious and their detection becomes increasingly challenging. Therefore, we propose HoloScope, which uses information from graph topology and temporal spikes to more accurately detect groups of fraudulent users. In terms of graph topology, we introduce ``contrast suspiciousness,'' a dynamic weighting approach, which allows us to more accurately detect fraudulent blocks, particularly low-density blocks. In terms of temporal spikes, HoloScope takes into account the sudden bursts and drops of fraudsters' attacking patterns. In addition, we provide theoretical bounds for how much this increases the time cost needed for fraudsters to conduct adversarial attacks. Moreover, HoloScope has a concise framework and sub-quadratic time complexity, making the algorithm reproducible and scalable. Extensive experiments showed that HoloScope achieved significant accuracy improvements on synthetic and real data, compared with state-of-the-art fraud detection methods.
Nicola Perra Networks and Time Network Science Institute 2018-05-21 | Networks, virtually in any domain, are dynamical entities. Think for example about social networks. New nodes join the system, others leave it, and links describing their interactions are constantly changing. However, due to absence of time-resolved data and mathematical challenges, the large majority of research in the field neglects these features in favor of static representations. While such approximation is useful and appropriate in some systems and processes, it fails in many others. Indeed, in the case of sexual transmitted diseases, ideas, and meme spreading, the co-occurrence, duration and order of contacts are crucial ingredients.During this talk, I will present a novel mathematical framework for the modeling of highly time-varying networks and processes evolving on their fabric. In particular, I will focus on epidemic spreading, random walks, and social contagion processes on temporal networks.
