Abstract
In social networks, once a user is more willing to influence her neighbors, a larger influence spread will be boosted. Inspired by the economic principle that people respond rationally to incentives, properly incentivizing users will lift their tendencies to influence their neighbors, resulting in a larger influence spread. However, this phenomenon is ignored in traditional IM studies. This paper presents a new diffusion model, IB-IC Model (Incentive-based Independent Cascade Model), to describe this phenomenon, and considers maximizing the influence spread under this model. However, this work faces great challenge under high solution quality and time efficiency. To tackle the problem, we propose AERIAL algorithm with solutions not worse than existing methods in high probability and \(O(n^2)\) average running time. We conduct experiments on several real-world networks and demonstrate that our algorithms are effective for solving IM Problem under IB-IC Model.
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Sun, Y., Wang, Q., Li, H. (2019). AERIAL: An Efficient Randomized Incentive-Based Influence Maximization Algorithm. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. https://doi.org/10.1007/978-3-030-26072-9_17
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DOI: https://doi.org/10.1007/978-3-030-26072-9_17
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