Title: Clustering in graphs with high clustering coefficients
Abstract: Many real world networks possess the so-called small world phenomenon where every node is relatively close to every other node and have a large clustering coefficient, i.e., friends of friends are likely friends. The task of learning an adequate similarity measure on various feature spaces often involves graphs with high clustering coefficients. We investigate the clustering effect in sparse clustering graphs by examining the structural and spectral properties as well as the enumeration of patterns. In addition, we consider random graph models for clustering graphs that can be used to analyze the behavior of complex networks.
Bio: Fan Chung Graham is a Distinguished Professor of Mathematics and Professor of Computer Science and Engineering at the University of California, San Diego. She is also the Paul Erdos Professor in Combinatorics. Her research interests are primarily in graph theory, combinatorics, and algorithmic analysis. She specializes in spectral graph theory, extremal graphs, the theory of quasi-randomness and the probabilistic analysis of complex networks. Recently her work includes local graph algorithms for complex networks and random walks based ranking algorithms. She was awarded the Allendoerfer Award by Mathematical Association of America in 1990. She is a member of the American Academy of Arts and Sciences and she is an academician of Academic Sinica. She is a fellow of American Mathematical Society and a SIAM fellow.