Targeted Influence Minimization in Social Networks

Xinjue Wang, Ke Deng, Jianxin Li, Jeffrey Xu Yu, Christian S. Jensen, Xiaochun Yang

Research output: Chapter in Book/Conference paperConference paperpeer-review

12 Citations (Scopus)

Abstract

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 (1−1/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.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
Subtitle of host publication22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
EditorsGeoffrey I. Webb, Dinh Phung, Mohadeseh Ganji, Lida Rashidi, Vincent S. Tseng, Bao Ho
Place of PublicationCham
PublisherSpringer
Pages689-700
Number of pages12
ISBN (Electronic)9783319930404
ISBN (Print)9783319930398
DOIs
Publication statusPublished - 19 Jun 2018
EventPacific-Asia Conference on Knowledge Discovery and Data Mining - Melbourne, Australia
Duration: 1 Jan 20116 Jun 2018
Conference number: 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10939 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining
Abbreviated titlePAKDD
Country/TerritoryAustralia
CityMelbourne
Period1/01/116/06/18

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