Selecting Multiple Network Spreaders based on Community Structure using Two-Phase Evolutionary Framework

Abstract

The identification of multiple network spreaders is an appropriate solution to spread information, ideas or diseases in many practical applications. For instance, in target marketing, the spreaders are selected from customer groups classified by similar purchase behaviors to advertise the products, and to optimize the allocation of limited resources. The community detection approaches intuitively are used to identify the community structures or social groups in a social/complex network. However, how to determine the number of community K is a difficult issue. Hence, two-phase evolutionary framework (TPEF) is proposed for automatically determining the number of community K and maximizing the modularity of communities. In the preliminary experiment, the LFR benchmark networks are used to test the proposed method, and to analyze the execution time, the community quality and the network spreading effect. The experiment results show that TPEF can perform well and produce the satisfied quality of community structures. The community detection approaches can be used to assist selecting the multiple network spreaders, and to gain the benefit in network spreading when the community structure is obvious. Furthermore, our results suggest that developing an index, a mechanism or a sampling technic is necessary to decide whether the community detection approaches are applied for selecting multiple network spreaders.