EVALUATION OF INFLUENTIAL NODES IN SOCIAL NETWORK

Abstract

Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networks, micro-blogging, or customer review sites etc. Such media often serve as platforms for information dissemination, improving the performance of web search, recommendations in collaborative filtering systems, spreading a technology in the market. It is well known that the interpersonal relationships (or ties or links) between individuals cause change or improvement in the social system because the decisions made by individuals are influenced heavily by the behavior of their neighbors. An interesting and key problem in social networks analysis is to discover the most influential nodes in the social network which can influence other nodes in the social network in a strong and deep way. Node position' is measure to evaluate the influence in social network. It enables to estimate how valuable the particular individual within the community is. Some time we need to give high priority to the recent activities, but the previous algorithm fails to consider time (recent and old) in activities for evaluation of influential nodes. In this thesis proposed an enhancement which will solve this problem. New algorithm is compared with previous algorithm for its performance. We also proposed a solution to Top-k nodes selection problem. In Top-k nodes problem we need to select. `k' nodes which will maximize the information dissemination. To solve the problem combination of influence values calculated and new algorithm proposed are employed.