Detecting network communities, i.e. subgraphs whose nodes have an appreciably larger probability to get connected than to other network nodes, is a fundamental problem in network science. Here I will discuss two major issues. First, 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. Second, I will show that neural embeddings can be used to efficiently detect communities.
The science of science is the investigation of science as a system, via analysis and modelling 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 normalising the raw number of cites. Also, I will discuss the impact of the COVID pandemic on science.
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