Mining of influencers in signed social networks: A memetic approach

Abstract

The tenacious unfurl of social networks and its unfathomable influence into the daily lives of users is overwhelming that tempts researchers to explore and analyze the domain of social influence mining. To date, most of the research tends to focus only on positive influence for discovering influencers however, in signed social networks (SSNs) where besides positive links there are negative links that ascertain the presence of negative influence also. Thus, it is essential to consider both positive and negative influences to mine influential nodes in SSNs. In this work, we propose a novel approach based on memetic algorithm (MA) for finding set of influential users in a SSN. Our contribution is twofold. First, we formulate a new fitness function termed as Status Influential Strength (SIS) grounded on status theory and strength of links between users. Next, we propose a new approach for Mining Influencers based on Memetic Algorithm (MIMA) in signed social networks. The performance of proposed approach is validated through various experiments conducted on real-world Epinions dataset and the results clearly establish the efficacy of our proposed approach.