Influence Maximization Algorithm Based on an Improved Sine and Cosine Optimization Search
XIONG Jinye1a, LI Ruqi1b, SONG Yurong1a, ZHANG Hui1a,2, JIANG Guoping1a
1. a. College of Automation and College of Artificial Intelligence; b. School of Computer Science,Nanjing University of Posts and Telecommunications, Nanjing 210023, China; 2. Jiangsu Manyun Software Technology Company Limited, Nanjing 210012, China
Abstract：In the influence maximization problem, existing methods based on greed and the centrality of network structure are difficult to strike a balance between accuracy and time complexity. Aiming at the limitations of existing algorithms, an improved sine-cosine optimization search influence maximization algorithm DSCA is proposed. This method takes into account the two-hop neighbors of nodes, and combines the position update concept of the optimization algorithm with dynamic small-range search to optimize a single node. The sine-cosine search, alignment and mutation operators are used to search nodes based on ordering. Experiments on four networks show that the proposed method has wider propagation range and lower time complexity than the advanced benchmark method, which proves the superiority and accuracy of the proposed algorithm.
熊锦烨, 李汝琦, 宋玉蓉, 张晖, 蒋国平. 基于改进正余弦优化搜索的影响力最大化算法[J]. 复杂系统与复杂性科学, 2026, 23(4): 19-26.
XIONG Jinye, LI Ruqi, SONG Yurong, ZHANG Hui, JIANG Guoping. Influence Maximization Algorithm Based on an Improved Sine and Cosine Optimization Search[J]. Complex Systems and Complexity Science, 2026, 23(4): 19-26.
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