Efficient Centrality Analysis in Large Scale Networks
Ohio State University
Abstract: Centrality metrics, such as closeness or betweenness, quantify how central a node is in a network. They have been successfully used to carry analysis for various purposes such as structural analysis of knowledge networks, power grid contingency analysis, quantifying importance in social networks, and analysis of covert networks. Several works which have been conducted to rapidly compute these metrics exist in the literature. The algorithm with the best asymptotic complexity to compute centrality metrics is believed to be asymptotically optimal. However, computing these metrics usually takes a lot of time. I will talk about some algorithmic and high-performance computing techniques to make the centrality analysis more efficient.
Bio: Kamer Kaya got his Ph.D. degree from Bilkent University in August, 2009. He then joined the Parallel Algorithms group in CERFACS (Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique), France, as a Post-graduate researcher. In September 2011, he started to work at Department of Biomedical Informatics at the Ohio State University. Since December 2012, he is a Research Assistant Professor in the same department. His research interests include parallel algorithms, high performance computing, bioinformatics, and cryptography.