当前位置: X-MOL 学术World Wide Web › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Efficient targeted influence minimization in big social networks
World Wide Web ( IF 3.7 ) Pub Date : 2020-03-19 , DOI: 10.1007/s11280-019-00748-z
Xinjue Wang , Ke Deng , Jianxin Li , Jeffery Xu Yu , Christian S. Jensen , Xiaochun Yang

An online social network can be used for the diffusion of malicious information like derogatory rumors, disinformation, hate speech, revenge pornography, etc. This motivates the study of influence minimization that aim to prevent the spread of malicious information. Unlike previous influence minimization work, this study considers the influence minimization in relation to a particular group of social network users, called targeted influence minimization. Thus, the objective is to protect a set of users, called target nodes, from malicious information originating from another set of users, called active nodes. This study also addresses two fundamental, but largely ignored, issues in different influence minimization problems: (i) the impact of a budget on the solution; (ii) robust sampling. To this end, two scenarios are investigated, namely unconstrained and constrained budget. Given an unconstrained budget, we provide an optimal solution; Given a constrained budget, we show the problem is NP-hard and develop a greedy algorithm with an \((1-\frac {1}{e})\)-approximation. More importantly, in order to solve the influence minimization problem in large, real-world social networks, we propose a robust sampling-based solution with a desirable theoretic bound. Extensive experiments using real social network datasets offer insight into the effectiveness and efficiency of the proposed solutions.

中文翻译:

大型社交网络中有针对性的有效影响最小化

在线社交网络可用于传播恶意信息,例如贬义谣言,虚假信息,仇恨言论,复仇色情等。这激发了对影响最小化的研究,旨在防止恶意信息的传播。与以前的影响最小化工作不同,本研究考虑了针对特定社交网络用户群体的影响最小化,即目标影响最小化。因此,目的是保护一组用户(称为目标节点)免受来自另一组用户(称为活动节点)的恶意信息的侵害。这项研究还解决了不同程度的影响最小化问题的两个基本的,但很大程度上忽视了,问题:()预算对解决方案的影响;(ii)稳健的抽样。为此,研究了两种方案,即不受约束的预算和受约束的预算。在预算不受限制的情况下,我们提供了最佳解决方案;给定受约束的预算,我们证明问题是NP难的,并开发了一个\((1- \ frac {1} {e})\)近似的贪婪算法。更重要的是,为了解决大型,现实世界的社交网络中的影响最小化问题,我们提出了一种基于鲁棒的基于采样的解决方案,并具有理想的理论界限。使用真实社交网络数据集进行的大量实验提供了对所提出解决方案的有效性和效率的了解。
更新日期:2020-03-19
down
wechat
bug