Pruning-enabled dynamic influence maximization using antlion optimization

Influence maximization (IM) is a widely studied topic in social network analysis that gives a reliable basis to select top nodes (seed set) to maximize the influence. IM has several real-world applications, such as advertising, political campaigns, profit maximization, etc. Existing literature suggests several algorithms for IM, including nature-inspired algorithms. In addition, most of the algorithms in IM consider static social networks. Existing studies show that antlion optimization (ALO) is known for its exploration abilities, and existing work in IM does not utilize it. Further, overlap influence reduces the overall influence in the network. To address the mentioned issues, for dynamic social networks, the proposed work suggests a novel algorithm (DALO-IM) for IM using ALO. The suggested strategy utilizes the previous computation during the dynamic traversal of the network. Further, this work suggests a prune-based strategy to overcome the problem of overlap influence. The experiments were conducted on eight datasets. The result analysis shows that the influence using the proposed algorithm is higher than the top-performing benchmark algorithm. Furthermore, this work conducted the ablation study to show the effectiveness of the suggested pruning strategy.