Skip to Main Content
Summary form only given. Social metrics have recently been considered to capture the degree of similarity in interests of the nodes as well as their “standing” within a community or network. In this talk some recent works-examples are briefly presented showing the potential benefits from incorporating social metrics in content replication, forwarding and placement. More specifically, a framework for assessing interest similarity is presented and applied to illustrate how similarity affects the effectiveness of content replication and forwarding. In addition, the widely adopted Betweenness Centrality metric is revisited and issues associated with its computation and appropriateness for content forwarding are discussed. Then, modifications and easily computable variants are introduced and their effectiveness is illustrated.