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PDNS-DODE: A discrete osprey optimization and differential evolution algorithm with pagerank diffusion neighborhood search for influence maximization in social networks

algorithms-and-analysis-of-algorithms
computer-networks-and-communications
data-mining-and-machine-learning
network-science-and-online-social-networks
social-computing

Abstract

Online social networks have become crucial platforms for marketing, leveraging their vast user bases and capacity for rapid information dissemination. Influence Maximization (IM) problem plays a central role in applications such as viral marketing and information propagation. Nevertheless, IM remains challenging due to the difficulty in balancing accuracy and efficiency with existing methods. To overcome these limitations, a discrete hybrid optimizer called PDNS-DODE was proposed. The algorithm incorporates an adaptive three-hop diffusion neighborhood search (PDNS) strategy based on PageRank centrality, which dynamically adjusts the search scope to enable broader global exploration and targeted local optimization. A PageRank-descending initialization strategy is introduced to improve population diversity. Furthermore, The Osprey Optimization Algorithm (OOA) and Differential Evolution (DE) are discretized with revised individual representation and update mechanisms for solving the influence maximization problem. Experimental evaluations on seven real-world social networks demonstrate that PDNS-DODE achieves a significant gain in influence spread. Compared to seven advanced baseline algorithms, the improvement ranges from 1.53% to 5.7% (Wilcoxon test, p < 0.05), while also maintaining high computational efficiency.
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