Online Influence Maximization under Decreasing Cascade Model

• URL: http://arxiv.org/abs/2305.15428v1
• Date: Fri, 19 May 2023 07:38:36 GMT
• Title: Online Influence Maximization under Decreasing Cascade Model
• Authors: Fang Kong, Jize Xie, Baoxiang Wang, Tao Yao, Shuai Li
• Abstract summary: We study online influence (OIM) under a new model of decreasing cascade (DC) In DC, the chance of an influence attempt being successful reduces with previous failures. We propose the DC-UCB algorithm, which achieves a regret bound of the same order as the state-of-the-art works on the IC model.
• Score: 18.536030474361723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study online influence maximization (OIM) under a new model of decreasing cascade (DC). This model is a generalization of the independent cascade (IC) model by considering the common phenomenon of market saturation. In DC, the chance of an influence attempt being successful reduces with previous failures. The effect is neglected by previous OIM works under IC and linear threshold models. We propose the DC-UCB algorithm to solve this problem, which achieves a regret bound of the same order as the state-of-the-art works on the IC model. Extensive experiments on both synthetic and real datasets show the effectiveness of our algorithm.

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