This paper proposes to utilize algorithms from the probabilistic graphical models domain for Peer-to-Peer rating of data items and for computing "social influence" 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 efficient computation of other node ranking methods, such as PageRank and Information Centrality.