DIFSoN: Discovering influential friends from social networks

Abstract

Social networks, which are made of social entities (e.g., individual users) linked by some specific types of interdependencies such as friendship, have become popular to facilitate collaboration and knowledge sharing among users. Such interactions or interdependencies can be dependent on or influenced by user characteristics such as connectivity, centrality, weight, importance, and activity in the networks. As such, some users in the social networks can be considered as highly influential to others. In this paper, we propose a computational model that integrates data mining with social computing to help users to discover influential friends from the social networks.