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100 user, she is trusting the other party not to send spam. When a user selects a winning bidder in an auction, she is trusting the other party to pay the winning amount, and the winning user is trusting the seller to produce the auctioned item. In a social network, the underlying user graph can potentially be used as a means to infer some level of trust in an unknown user [86], to check the validity of a public key certificate [110], and to classify potential spam [57]. In all of these, trust is computed as a function of the path between the source and target user. Our findings have interesting implications for trust inference algorithms. The tight core coupled with link reciprocity implies that users in the core appear on a large number of short paths. Thus, if malicious users are able to penetrate the core, they can skew many trust paths (or appear highly trustworthy to a large fraction of the network). However, these two properties also lead to small path lengths and many disjoint paths, so the trust inference algorithms should be adjusted to account for this observation. In particular, given our data, an unknown user should be highly trusted only if multiple short disjoint paths to the user can be discovered. The correlation in link degrees implies that users in the fringe will not be highly trusted unless they form direct links to other users. The “social” aspect of these networks is self-reinforcing: in order to be trusted, one must make many “friends”, and create many links that will slowly pull the user into the core.PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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