COMPUTATIONAL STRATEGIES FOR TOPIC TRUST PROPAGATION BASED ON K-LEVEL NEIGHBORS

Que Tran Dinh

Abstract


Topic trust in social networks is defined by means of a function of
trust degrees, which are estimated via interaction experience and user
interests. The computation of such a function is based on propagation
of trust values along paths with neighbor nodes and thus own highly
computational cost. In this paper, we first consider various strategies for
estimating topic trust based on a hierarchy of users with k-level neighbors. Then we introduce algorithms for computing topic trust values
w.r.t. these strategies.

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