An Asynchronous Distributed Cooperative Coevolutionary Algorithm for Multilayer Influence Maximization

The influence maximization (IM) problem in large-scale social networks has attracted great attention. Considering the interactions among multiple online social platforms, the multilayer IM problem poses further challenges ($\rm i.e.,$ high-simulation burden and low-optimization quality). To solve these problems, this article proposes a susceptible-exposed-infected1-infected2-infected12-vigilant (SE3IV) model to simulate the information spreading process in multilayer networks. The spreading dynamic is modeled by mean-field equations considering the effect of cross-layer propagation. To optimize the multilayer information maximization modeled by SE3IV, an asynchronous distributed cooperative coevolutionary algorithm (ADCA) is proposed. To improve the efficiency of the algorithm in multilayer networks, the multilayer community detection first decompresses the network into a single layer by dimension-based method. Then, the Louvain method is adopted to decompose the problems into subcomponents with lower dimensionality. The populations with the same size evolve corresponding subcomponents in an asynchronous and distributed way based on the pool model. Besides, an asynchronous communication mechanism is devised to manage the communication among the shared pool. An adaptive seeds regulation strategy is designed to adjust the number of seeds of subcomponents. Numerous experiments on different networks show that ADCA possesses good scalability and efficiency, especially in large-scale networks.