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P2P Authority Analysis for Social Communities

PageRank-style authority analyses of Web graphs are of great importance for Web mining. Such authority analyses also apply to hot "Web 2.0" applications that exhibit a natural graph structure, such as social networks (e.g., MySpace, Facebook) or tagging communities (e.g., Flickr, Del.icio.us). Finding the most trustworthy or most important authorities in such a community is a pressing need, given the huge scale and also the anonymity of social networks. 
 
Computing global authority measures in a Peer-to-Peer (P2P) collaboration of autonomous peers is a hot research topic, in particular because of the incomplete local knowledge of the peers, which typically only know about (arbitrarily overlapping) sub-graphs of the complete graph. We demonstrate a self-organizing P2P collaboration that, based on the local sub-graphs, efficiently computes global authority scores. In hand with the loosely-coupled spirit of a P2P system, the computation is carried out in a completely asynchronous manner without any central knowledge or coordinating instance. We demonstrate the applicability of authority analyses to large-scale distributed systems.