Genetically Driven Optimal Selection of Opinion Spreaders in Complex Networks

Abstract

The problem of influence maximization (IM) represents a major challenge for modern network science, with direct applicability in political science, economy, epidemiology, and rumor spreading. Here, we develop a novel computational intelligence framework (GenOSOS) based on genetic algorithms with emphasis on the optimal layout of spreader nodes in a network. Our algorithm starts with solutions consisting of randomly selected spreader nodes; then, by defining custom original crossover and mutation operators, we are able to obtain, in a short number of genetic iterations, nearly optimal solutions in terms of the nodes’ topological layout. Experiments on both synthetic and real-world networks show that the proposed GenOSOS algorithm is not only a viable alternative to the existing node centrality approach, but that it outperforms state of the art solutions in terms of spreading coverage. Specifically, we benchmark GenOSOS against graph centralities such as node degree, betweenness, PageRank and k-shell using the SIR epidemic model, and find that our solution is, on average, 11.45% more efficient in terms of diffusion coverage.