Influence Nodes Identifying Method via Community-Based Backward Generating Network Framework

Xiaoyang Liu, Shu Ye, Giacomo Fiumara, Pasquale De Meo

Abstract: Traditional methods for influential node identification usually require time consuming network traversal to select the candidate node set. In this article we propose a new influence nodes identification method, called Community-based Backward Generating Network (CBGN). First, the influence maximization framework is built by integrating community detection and Backward Generation Network (BGN); then, nodes in each community are selected using a new method, called imp_BGN, that uses graph traversal to assist the construction of BGN. The ultimate goal of the network generation method is to find a sequence of nodes that can minimize the cost function, and to select high influential nodes without restoring the original network during network construction. finally, an improved submodular CELF (Cost Effective Lazy Forward) algorithm is proposed to hunt for the final seed node from the candidate node pool considering the location relation and structural similarity among nodes. Experimental results show that: in the SIR (susceptible-infected-recovered) model experiment, compared with the benchmark methods, the infection scale of the proposed CBGN method in 6 real networks is improved by 0.45%, 0.59%, 0.84%, 1.05%, 0.71% and 0.14%, respectively.