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A Budged Framework to Model a Multi-round Competitive Influence Maximization Problem

The concept of competitive influence maximization has attracted considerable research interest in recent years. Modeling the behavior of social network members is a key challenge in this regard. This kind of forwarding occurs from newly activated nodes to their not yet activated neighbors, especially when some competitors try to gain maximum influence over the network. For competitors, the main objective is to find which potential members are most valuable to them and how many resources they should allocate to them in order to get as much influence as possible. Previous studies on competitive influence maximization tended to focus on the single-shot game without considering how budget allocation affects the outcome. We are interested in maximizing the total number of activated nodes through multi-round competitive influence maximization, where each competitor has to determine the locations and budget amount to invest simultaneously and repeatedly within a given total budget. In this paper, we propose a tree-approximate game-theoretical framework and introduce the new measurement as a dynamic weight. This measure allows us to isolate the most influential member with a high degree of accuracy. Our simulation study demonstrates the effectiveness of our approach in solving a multi-round, learning-based CIM problem.