Uploaded April 2025 | Updated September 2026, 2 weeks ago
Santo Fortunato, Indiana University Bloomington
Detecting network communities, i.e. subgraphs whose nodes have an appreciably larger probability to get connected to each other than to other nodes of the network, is a fundamental problem in network science. Here I will discuss three major issues. First, I will address the limits of the most popular class of clustering algorithms, those based on the optimization of a global quality function, like modularity maximization. Second, I will critically review the process of validation, probably the single most important issue of network community detection, as it implicitly involves the concept of community, which is ill-defined. I will discuss the importance of using realistic benchmark graphs with built-in community structure as well as the role of metadata. Finally, I will show that neural embeddings can be used to efficiently detect communities.
Science of science is the investigation of science as a system, via analysis and modeling of data on scientists and their interactions. I will show that the distributions of citations of papers published in the same discipline and year rescale to a universal curve, by properly normalizing the raw number of cites. Also, I will discuss the impact of the COVID pandemic on science.
Learn more, follow us on social media and check out our podcasts:
https://linktr.ee/sfiscience
Santo Fortunato, Indiana University Bloomington
Detecting network communities, i.e. subgraphs whose nodes have an appreciably larger probability to get connected to each other than to other nodes of the network, is a fundamental problem in network science. Here I will discuss three major issues. First, I will address the limits of the most popular class of clustering algorithms, those based on the optimization of a global quality function, like modularity maximization. Second, I will critically review the process of validation, probably the single most important issue of network community detection, as it implicitly involves the concept of community, which is ill-defined. I will discuss the importance of using realistic benchmark graphs with built-in community structure as well as the role of metadata. Finally, I will show that neural embeddings can be used to efficiently detect communities.
Science of science is the investigation of science as a system, via analysis and modeling of data on scientists and their interactions. I will show that the distributions of citations of papers published in the same discipline and year rescale to a universal curve, by properly normalizing the raw number of cites. Also, I will discuss the impact of the COVID pandemic on science.
Learn more, follow us on social media and check out our podcasts:
https://linktr.ee/sfiscience
