Abstract: 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.
The College of Arts