A Distributed Method for Negative Content Spread Minimization on Social Networks

Ruidong Yan, Zhenhua Guo, Weili Wu, Baoyu Fan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Currently, social networks have emerged as significant platforms for individuals to share personal information and social content. However, it is important to recognize that social networks have both positive and negative aspects. To effectively address the dissemination of negative social content such as rumors and misinformation, it is crucial to implement strategies that involve immediate blocking of associated links. This paper introduces a Negative Content Spread Minimization (NCSM) problem, which aims to minimize the spread of negative content by removing a set of edges from the network. We begin by demonstrating the NP-hardness of the NCSM problem through reduction from the Knapsack Problem. Furthermore, we establish that the objective function is not submodular under the Independent Cascade model. To address, we employ a distributed method which includes community partition and influential edges selection. The advantage of this approach is to reduce computational overhead by selecting key edges in parallel in each community. To evaluate proposed algorithm, we conduct experiments using real-world datasets and compare them against existing methods. The experimental results demonstrate that our method outperforms state-of-the-art algorithms.

Original languageEnglish
Title of host publicationAlgorithmic Aspects in Information and Management - 18th International Conference, AAIM 2024, Proceedings
EditorsSmita Ghosh, Zhao Zhang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages157-169
Number of pages13
ISBN (Print)9789819777976
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event18th International Conference on Algorithmic Aspects in Information and Management, AAIM 2024 - Virtual, Online
Duration: 21 Sept 202423 Sept 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15179 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Algorithmic Aspects in Information and Management, AAIM 2024
CityVirtual, Online
Period21/09/2423/09/24

Keywords

  • Social network
  • negative content spread
  • non-submodularity

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