Clustering algorithms for networks
Computer Engineering Department, Floor 1, Class 102
Wednesday, 30 December 2015
11:30 - 12:30
The problem of detecting communities in a graph (a.k.a. clustering) is one of the most studied inference problems, given its simple formulation and widespread diffusion among several disciplines. Spectral methods are among the most popular nonparametric approaches to clustering. These methods classify nodes according to the eigenvectors of a matrix associated with the graph, for instance, its adjacency matrix or Laplacian. While standard spectral clustering works well when the graph is sufficiently dense or is regular, it is significantly suboptimal for sparse graphs or generally graphs with highly heterogeneous degrees. Recently, there has a been a line of interesting work to address these cases. I will provide an overview of these algorithms.
Adel Javanmard is an assistant professor in the department of Data Sciences and Operation, Marshall School of Business at the University of Southern California. Prior to joining USC, he was a postdoctoral research fellow for a year at the Center for Science of Information, with worksite at UC Berkeley and Stanford University. He received his PhD and MS in Electrical Engineering from Stanford University in 2014 and 2010 advised by Andrea Montanari. Before that, he received BSc degrees in Electrical Engineering and Pure Math at Sharif University of Technology in 2009. His research interests are broadly in the area of high-dimensional statistics, machine learning, optimization, and graphical models. Adel has won several awards and fellowships, including the Thomas Cover dissertation award from IEEE Society (2015), the CSoI Postdoctoral Fellowship (2014), the Caroline and Fabian Pease Stanford Graduate Fellowship (2010-2012), and the Stanford Electrical Engineering Fellowship (2009). Adel was a finalist for the best student paper award at IEEE International Symposium on Information Theory (ISIT) in 2011 and 2012.