Abstract
Influence maximization (IM) in social networks aims to figure out the best subset of seed nodes which have maximum cascading influence under a diffusion model. This paper proposes a hybrid Community-based Simulated Annealing (ComSA) approach for the IM problem. A community detection algorithm is employed to segregate the entire social network structure into some more deeply clustered communities. Thereafter, a degree-based metric has been used to select the candidate pool from each community by excluding less influential nodes at the preliminary data preprocessing phase. A community-based seed initialization and neighborhood search technique have been proposed. To speed up the convergence of stable solutions in Simulated Annealing approach, a greedy hill climbing strategy is also implemented instead of using probabilistic based solution acceptance processes. Experimental results on four real-world datasets show that our proposed algorithm has comparable solution with greedy and outperforms the other existing meta-heuristic approaches.
Published Version
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