Resampling-based Framework for Estimating Node Centrality of Large Social Network
Hiroshi Motoda
Doctor
ISIR, Osaka University and AFOSR/AOARD
Abstract: We address a problem of efficiently estimating value of a centrality measure for a node in a large social network only using a partial network generated by sampling nodes from the entire network. To this end, we propose a resampling-based
framework to estimate the approximation error defined as the difference betwee, the true and the estimated values of the centrality．We experimentally evaluate the fundamental performance of the proposed framework using the closeness and betweenness centralities on three real world networks, nd show that it allows us to estimate the approximation error more tightly and more precisely with the confidence level of 95％even for a small partial network compared with the standard error traditionally used, and that we could potentially identify top nodes and possibly rank them in a given centrality measure with high confidence level only from a small partial network.
Bio: Hiroshi Motoda was a professor in the division of Intelligent Systems Science at ISIR of Osaka University since 1996 until March, 2006. Before joining the university, he had been with Hitachi since 1967, participated in research on nuclear reactor core management, control and design of nuclear power reactors, expert systems for nuclear power plant diagnosis．He then continued to work on machine learning and knowledge acquisition, and has extended his research to scientific knowledge discovery and data mining. Recently he has been working on social network analysis while managing several projects. He is now an honorary member of the steering committee of Pacific Rim International Con~rence of Artificial Intelligence, a life long member of the steering committee of Pacific Asian Conference of Knowledge Discovery and Data Mining.