Networks, communities, and the science of science
Prof. Santo Fortunato
Director, Indiana University Network Science Institute (IUNI)
Professor, Luddy School of Informatics, Computing,and Engineering
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. 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. Validation is 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. I will also show that neural embeddings can be used to efficiently detect communities.
Science of science is the investigation of science as a system. 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. Finally, I will introduce ongoing projects, focusing on the evolution of science, social contagion and the impact of COVID in science.
Hosted by Prof. Eskildsen