AIGCrank: A new adaptive algorithm for identifying a set of influential spreaders in complex networks based on gravity centrality

Abstract

Abstract The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process. This problem finds many practical applications in numerous areas such as information dissemination, epidemic immunity, and viral marketing. However, most existing influence maximization algorithms are limited by “rich-club” phenomenon and are thus unable to avoid the influence overlap of seed spreaders. This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy, named AIGCrank, to identify a set of influential seeds. Specifically, the gravity centrality jointly employs the neighborhood, network location and topological structure information of nodes to evaluate each node’s potential of being selected as a seed. We also present a recursive ranking strategy for identifying seed nodes one-by-one. Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.