A Study on Influence Maximizing Based on Two Rounds of Filtration Metric in Social Networks

The influence maximization problem is discovering a seed set of nodes in a social network and making the spread as large as possible based on influence propagation. The current related algorithm based on the greedy strategy maintains a better influence propagation but has high time complexity and is not very scalable. This paper proposes a new method to solve the influence maximization problem by reducing the time complexity, called the Two Rounds of Filtration Metric (TRFM) algorithm. The main work is as follows:(1) A regional node metric is proposed based on the local topology to measure the nodes, which reduces the evaluation time. (2) The submodular characteristic is applied to discover the TOP-K seed node set from the candidate node set; meanwhile, the evaluation measurement in the whole network maintains a better influence propagation. The experimental results on the actual data set verify the effectiveness of the TRFM algorithm. © (2024), (International Journal of Network Security). All rights reserved.