A Study of Delayed Competitive Influence Propagation Based on Shortest Path Calculation

In social network applications, competitive influence propagation often exhibits a certain degree of time lag. In the scenario of positive and negative competitive propagation studied in this paper, under the premise that negative nodes are activated first, how to find a set of positive seed nodes to participate in competitive propagation is studied, aiming to minimize the spread of negative influence. In the current study, the time complexity of the improved algorithms based on greedy strategies is high, which limits their scope of application in practical scenarios; some heuristic algorithms achieve better scalability, but there is still much room for improvement. Therefore, this paper proposes a new method to solve the influence propagation problem of delayed competition, also known as a heuristic propagation factor evaluation algorithm (HeuPFE). The main process is as follows: (1) We build a shortest path snapshot for the nearest propagation region of negative nodes and reduce the search space of competing nodes. (2) Then, we construct a node propagation factor evaluation method based on this shortest negative-oriented node path snapshot to reduce computational complexity. Comparing results with those of traditional heuristic algorithms, we carry out experiments on real datasets and verify the effectiveness of the proposed method.