Binomial Regressive Influence Behavior Ranking for Virtual Community Formation in Social Network

Abstract

A Social Network (SN) is a website which permits people to share the data about their personal or business endeavor to form the virtual community. Due to communication between the users in the SN, a similar users behavior identification causes a fundamental issue. The existing techniques still encounter the problem of identifying similar behavior accurately. Therefore, Statistic Dice Similarity Based Probabilistic Binomial Regression and Ranking (SDS-PBRR) method introduced. First, the similarity value between the users behaviors is calculated using Statistic Dice Similarity Coefficient (SDSC). Second, Probabilistic Binomial Regression Analysis is carried out to evaluate the similarity value and to classify the users as Influencing Behavior (IB) or Non-Influencing Behavior (N-IB) minimum error rate in the SN. Last, firefly algorithm is applied to perform a ranking process for discovering the level of IB users in the SN. The simulation results show that SDS-PBRR method increases the True Positive Rate (TPR) and minimizes the False Positive Rate (FPR) as well as execution time