Adaptive Influence Maximization in Dynamic Social Networks Article Swipe
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· 2016
· Open Access
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· DOI: https://doi.org/10.1109/tnet.2016.2563397
· OA: W3122282375
For the purpose of propagating information and ideas through a social\nnetwork, a seeding strategy aims to find a small set of seed users that are\nable to maximize the spread of the influence, which is termed as influence\nmaximization problem. Despite a large number of works have studied this\nproblem, the existing seeding strategies are limited to the static social\nnetworks. In fact, due to the high speed data transmission and the large\npopulation of participants, the diffusion processes in real-world social\nnetworks have many aspects of uncertainness. Unfortunately, as shown in the\nexperiments, in such cases the state-of-art seeding strategies are pessimistic\nas they fails to trace the dynamic changes in a social network. In this paper,\nwe study the strategies selecting seed users in an adaptive manner. We first\nformally model the Dynamic Independent Cascade model and introduce the concept\nof adaptive seeding strategy. Then based on the proposed model, we show that a\nsimple greedy adaptive seeding strategy finds an effective solution with a\nprovable performance guarantee. Besides the greedy algorithm an efficient\nheuristic algorithm is provided in order to meet practical requirements.\nExtensive experiments have been performed on both the real-world networks and\nsynthetic power-law networks. The results herein demonstrate the superiority of\nthe adaptive seeding strategies to other standard methods.\n
