ECLP: Friend Recommendation Using Ensemble Approach for Detecting Communities Performing Link Prediction

Abstract

Social networks provide a variety of online services that play an important role in new connections among members to share their favorite media, document, and opinions. For each member, these networks should precisely recommend (predict) the link of members with the highest common interests. Because of the huge volume of users with different types of information, these networks encounter challenges such as dispersion and accuracy of link prediction. Moreover, networks with numerous users have the problem of computational and time complexity. These problems are caused because all the network nodes contribute to calculations of link prediction and friend suggestions. In order to overcome these drawbacks, this paper presents a new link prediction scheme containing three phases to combine local and global network information. In the proposed manner, dense communities with overlap are first detected based on the ensemble node perception method which leads to more relevant nodes and contributes to the link prediction and speeds up the algorithm. Then, these communities are optimized by applying the binary particle swarm optimization method for merging the close clusters. It maximizes the average clustering coefficient (ACC) of the whole network which results in an accurate and precise prediction. In the last phase, relative links are predicted by Adamic/Adar similarity index for each node. The proposed method is applied to Astro-ph, Blogs, CiteSeer, Cora, and WebKB datasets, and its performance is compared to state-of-the-art schemes in terms of several criteria. The results imply that the proposed scheme has a significant accuracy improvement on these datasets.