Aaron Clauset from University of Colorado at Boulder will give a specially scheduled Applied Mathematics Colloquium on Tuesday February 23, 2016 at 4 pm in Davie Hall 0112 entitled “Inferring community structure in networks with metadata.” See the abstract for the talk below.
Abstract: For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network, geographic location of nodes in the Internet, or cellular function of nodes in a gene regulatory network. In this talk, I’ll show how this “metadata” can be used to improve our analysis and understanding of network structure. I’ll focus in particular on the problem of community detection in networks and present a mathematically principled approach, based on a generalization of the stochastic block model, that combines a network and its metadata to detect communities more accurately than can be done with either alone. Crucially, the method does not assume that the metadata are correlated with the communities we are trying to find. Instead the method learns whether a correlation exists and correctly uses or ignores the metadata depending on whether they contain useful information. The learned correlations are also of interest in their own right, allowing us to make predictions about the community membership of nodes whose network connections are unknown. I’ll demonstrate the model on synthetic networks with known structure, where the method performs better than any algorithm without metadata, and on real-world networks, large and small, drawn from social, biological, and technological domains.