Cognitive social network analysis for supporting the reliable decision-making process

With the advancement of the Internet and web technologies, social networks have gained attention as a new paradigm for user-centered information systems. As the amount of accessible information increases, the need for personalized information increases as well. Under such circumstances, social networks that are based on trust between users are increasingly utilized to provide efficient and reliable information management. This paper proposes the cognitive social network analysis that analyzes relationships between users with typical properties. The proposed approach analyzes users’ habitual activities and creates a local social network. The framework then integrates the local networks via the friend of a friend, thereby creating a global social network. User relationships in the global network are reinforced to maximize information sharing. To evaluate the performance of the information shared in the proposed autonomic cognitive social network framework, the accuracy of the information associated with social network was measured using the ROC Curve. In future, we should analyze the social influence factors from relationship between community and users.