The best hop diffusion method for dynamic relationships under the independent cascade model

Liqing Qiu, Yuying Liu, Xiuliang Duan

Abstract: The goal of the influence maximization problem is to identify a seed set that maximizes the number of users who will be impacted. This issue is critical to the spread of information in social networks. The greedy and heuristic algorithms are two prominent algorithms for this problem. The greedy algorithms are usually effective but they have high computational complexity. The heuristic algorithms are tendly efficient while the accuracy is low. The major goal of this study is to strike a compromise between efficacy and efficiency in selecting k influential nodes to maximize influence propagation while keeping the expense of doing so to a minimal. This paper proposes an improved heuristic algorithm, called the Best Hop for Independent Cascade Model (BHICM). This approach presents a dynamic relationship strategy between propagation probability and hop count that avoids processing the seed node’s neighbors. Afterwards, the proposed algorithm defines the diffusion score as the criterion for selecting seed nodes, which ensures its accuracy and efficiency. According to experimental results on four genuine social networks, the proposed approach exceeds all comparison algorithms in terms of accuracy while maintaining an acceptable running time.