Learning Large-Scale Patterns in Complex Networks
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Networks provide a rich and mathematically principled approach to characterizing the structure of complex social and biological systems. A common step in understanding the structure and function of real-world networks is to characterize their large-scale organizational pattern via community detection, in which we aim to find a network partition that groups together vertices with similar connectivity patterns. Modern networks, however, often include rich auxiliary information, in the form of edge weights, vertex attributes, multi-partite structures, and edges that vary over time, and we often wish to incorporate these details into the network analysis.
In this talk, I will describe a general framework, based on the popular stochastic block model, for inferring large-scale patterns in complex networks. This approach recasts the problem of community detection as one of statistical inference, which allows the use of powerful tools from statistics, physics and machine learning. Furthermore, this approach can naturally capture a wide variety of specific large-scale patterns as special cases, and can be extended to learn from most types of auxiliary information. As positive examples of this approach, I will describe recent work from my group on inferring communities in edge-weighted networks or in bipartite networks, and on change-point detection in evolving networks.
Aaron Clauset is an Assistant Professor in the Department of Computer Science and the BioFrontiers Institute at the University of Colorado Boulder, and is External Faculty at the Santa Fe Institute. He received a PhD in Computer Science, with distinction, from the University of New Mexico, a BS in Physics, with honors, from Haverford College, and was an Omidyar Fellow at the prestigious Santa Fe Institute.
He is an internationally recognized expert on network science and the empirical analysis of complex systems. His work has appeared in prestigious scientific venues like Nature, Science, PNAS, JACM, AAAI, ICML, STOC, SIAM Review, and Physical Review Letters, and has been covered in the popular press by the Wall Street Journal, The Economist, Discover Magazine, New Scientist, Wired, Miller-McCune, the Boston Globe and The Guardian.