发表状态 | 已发表Published |
题名 | Adaptive Influence Maximization: If Influential Node Unwilling to Be the Seed |
作者 | |
发表日期 | 2021-06-01 |
发表期刊 | ACM Transactions on Knowledge Discovery from Data
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ISSN/eISSN | 1556-4681 |
卷号 | 15期号:5 |
摘要 | Influence maximization problem attempts to find a small subset of nodes that makes the expected influence spread maximized, which has been researched intensively before. They all assumed that each user in the seed set we select is activated successfully and then spread the influence. However, in the real scenario, not all users in the seed set are willing to be an influencer. Based on that, we consider each user associated with a probability with which we can activate her as a seed, and we can attempt to activate her many times. In this article, we study the adaptive influence maximization with multiple activations (Adaptive-IMMA) problem, where we select a node in each iteration, observe whether she accepts to be a seed, if yes, wait to observe the influence diffusion process; if no, we can attempt to activate her again with a higher cost or select another node as a seed. We model the multiple activations mathematically and define it on the domain of integer lattice. We propose a new concept, adaptive dr-submodularity, and show our Adaptive-IMMA is the problem that maximizing an adaptive monotone and dr-submodular function under the expected knapsack constraint. Adaptive dr-submodular maximization problem is never covered by any existing studies. Thus, we summarize its properties and study its approximability comprehensively, which is a non-trivial generalization of existing analysis about adaptive submodularity. Besides, to overcome the difficulty to estimate the expected influence spread, we combine our adaptive greedy policy with sampling techniques without losing the approximation ratio but reducing the time complexity. Finally, we conduct experiments on several real datasets to evaluate the effectiveness and efficiency of our proposed policies. |
关键词 | adaptive dr-submodularity Adaptive influence maximization approximation algorithm integer lattice sampling techniques social networks |
DOI | 10.1145/3447396 |
URL | 查看来源 |
收录类别 | SCIE |
语种 | 英语English |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering |
WOS记录号 | WOS:000668447500011 |
Scopus入藏号 | 2-s2.0-85108965690 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://repository.uic.edu.cn/handle/39GCC9TT/9094 |
专题 | 个人在本单位外知识产出 |
通讯作者 | Guo, Jianxiong |
作者单位 | 1.The University of Texas,Dallas,United States 2.The University of Texas,Dallas,United States |
推荐引用方式 GB/T 7714 | Guo, Jianxiong,Wu, Weili. Adaptive Influence Maximization: If Influential Node Unwilling to Be the Seed[J]. ACM Transactions on Knowledge Discovery from Data, 2021, 15(5). |
APA | Guo, Jianxiong, & Wu, Weili. (2021). Adaptive Influence Maximization: If Influential Node Unwilling to Be the Seed. ACM Transactions on Knowledge Discovery from Data, 15(5). |
MLA | Guo, Jianxiong,et al."Adaptive Influence Maximization: If Influential Node Unwilling to Be the Seed". ACM Transactions on Knowledge Discovery from Data 15.5(2021). |
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