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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
ISSN/eISSN1556-4681
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
DOI10.1145/3447396
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
被引频次:21[WOS]   [WOS记录]     [WOS相关记录]
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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