Abstract
Given a Social Network how to select a small number of influential users to maximize the influence in the network has been studied extensively in the past two decades and formally referred to as the Influence Maximization Problem. Among most of the existing studies, it has been implicitly assumed that there exists a single probability value that represents the influence probability between the users. However, in reality, the influence probability between any two users is dependent on the context (formally referred to as tag e.g.; a sportsman can influence his friends related to any news related to sports with high probability). In this paper, we bridge the gap by studying the Tag-Based Influence Maximization Problem. In this problem, we are given with a social network where each edge is marked with one probability value for every tag and the goal here is to select k influential users and r influential tags to maximize the influence in the network. First, we define a tag-based influence function and show that this function is bi-submodular. We use the orthent-wise maximization procedure of bi-submodular function which gives a constant factor approximation guarantee. Subsequently, we propose a number of efficient pruning techniques that reduces the computational time significantly. We perform an extensive number of experiments with real-world datasets to show the effectiveness and efficiency of the proposed solution approaches.
The work of Dr. Suman Banerjee is supported with the Seed Grant sponsored by the Indian Institute of Technology Jammu (Grant No.: SG100047).
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Tekawade, A., Banerjee, S. (2023). Influence Maximization with Tag Revisited: Exploiting the Bi-submodularity of the Tag-Based Influence Function. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14176. Springer, Cham. https://doi.org/10.1007/978-3-031-46661-8_51
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DOI: https://doi.org/10.1007/978-3-031-46661-8_51
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