This paper proposes to utilize algorithms from the probabilistic graphical models domain for Peer-to-Peer rating of data items and for computing \u201csocial in\ufb02uence\u201d of nodes in a Peer-to-peer social network. We evaluate the practicality of our approach using large scale simulations over a MSN Live Messenger subgraph consisting of about a million nodes. Our algorithms are general since they can be used for Peer-to-peer monitoring and for the ef\ufb01cient computation of other node ranking methods, such as PageRank and Information Centrality.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper proposes to utilize algorithms from the probabilistic graphical models domain for Peer-to-Peer rating of data items and for computing \u201csocial in\ufb02uence\u201d of nodes in a Peer-to-peer social network. We evaluate the practicality of our approach using large scale simulations over a MSN Live Messenger subgraph consisting of about a million nodes. 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