HEC: a hybrid evolutionary-CELF algorithm for enhanced influence maximization in social networks

Abstract

In the realm of influence maximization within social networks, the hybrid evolutionary-CELF (HEC) algorithm offers a novel approach by integrating evolutionary optimization techniques with the efficiency of the cost-effective lazy forward (CELF) algorithm. This paper presents the HEC algorithm, which enhances the process of selecting a seed set of nodes to maximize influence spread by combining the CELF algorithm’s computational efficiency with evolutionary strategies such as crossover and mutation. We demonstrate that this hybrid approach outperforms existing methods, including greedy algorithms, CELF, and cuckoo search, in terms of both influence spread and computational efficiency. Experimental results on various network structures highlight the superior performance of the HEC algorithm in balancing exploration and exploitation. The proposed method provides a robust solution for large-scale influence maximization problems, offering significant improvements in effectiveness and efficiency.