Community Detection in Multilayer Networks: Algorithms and Applications Video

Modern data analysis and processing tasks typically involve large sets of graph structured data, where the structure carries critical information. Typically, graphs are used as mathematical tools to describe the interactions between the different entities in the system. Characterizing the meso-scale organization, i.e. the community structure, is an important task in network science. Community detection aims to partition the network into sets of nodes that are densely connected internally but sparsely connected to other dense sets of nodes. Most of the current work on community detection focuses on static networks. However, in many contemporary applications, this relatively simple structure cannot capture the diverse nature of the networks, e.g. temporal networks where the structure changes with time and multilayer networks where the nodes may interact through different mechanisms. Therefore, there is a need to develop community detection methods for these high dimensional networks. In this talk, I will present matrix and tensor factorization methods to detect the community structure in temporal and multilayer networks. The proposed approaches formulate community detection as regularized matrix/tensor decomposition. Different applications of this framework for analyzing high-dimensional social and biological networks will be highlighted.