Online Sampling of High Centrality Individuals in Social Networks

In this work, we investigate the use of online or “crawling” algorithms to sample large social networks in order to determine the most influential or important individuals within the network (by varying definitions of network centrality). We describe a novel sampling technique based on concepts from expander graphs. We empirically evaluate this method in addition to other online sampling strategies on several realworld social networks. We find that, by sampling nodes to maximize the expansion of the sample, we are able to approximate the set of most influential individuals across multiple measures of centrality.

• Type: Conference
• Year: 2010
• Where: PAKDD
• Authors: Arun S. Maiya, Tanya Y. Berger-Wolf
• Keywords: Data Mining, Large Social Networks, Online Sampling Strategies, PAKDD 2010, Social Networks