INFORMATION FLOW MODEL FOR KEYWORD BASED SOCIAL NETWORKS

Abstract

In online social networks (OSNs), user connections can be represented as a network. The network formed has distinct properties that distinguish it from other network topologies. In this work, we considered keyword based social network topology where each edge has a trust value associated with it to represent the mutual relationship between the corresponding nodes. Users have keywords as their profile attributes that have policies associated, with them to define abstractly the flow of keyword information and the accessibility to other users in the network. One of the metrics in the study of keyword based social networks is trust. It is an important yet complex and little understood aspect of the dyadic relationship between two entities. Trust plays an important role in the formation of coalitions in social networks and in determining how high value of information flows through the network. We make two main contributions in this thesis. First, we develop an information flow model to disseminate keyword information when users add keywords as their profile attributes. It is based on a linear combination of topological distance and -trust metrics. Second, we presented algorithmically quantifiable measures of trust based on communication behavior. A typical social network consists of actors (individuals) and communications between them (phone calls, emails, blog posts, etc). The challenge is to quantify trust only on the basis of the observed communication behavior (a portion of the interactions between entities). n