Efficient Implementation of Anchored 2-core Algorithm

Abstract

Often graph theory is used to model and analyze different behaviors of networks including social networks. Nowadays, social networks have become very popular and social network providers try to expand their networks by encouraging people to stay engaged and active. Studies show that engagement and activities of people in social networks influence engagement of their connections. This behavior has been modeled by the k-core problem in graph theory with the assumption that a person stays active in the network if he or she has k or more connections. In the above model if a person drops out, his or her friends can become discouraged and they might also drop out. An approach called anchored k-core algorithm has been introduced lately that prevents a cascade of drop-outs by finding nodes which have the most influence on their connections and rewarding them to stay in the network. In this work, an efficient implementation of the anchored 2-core approach has been proposed. The proposed implementation method was applied to a set of real world network data that includes very large graphs with millions of links. The results show that with only a few anchors, it is possible to save hundreds of nodes for the 2-core graph. Also, the execution time of our implementation is in order of minutes for huge datasets which proves the efficiency of our implementation.