Scholarlink

<p>Influence maximization (IM) is a well-known problem in social network analysis, which aims to identify a strategic set of seeds to maximize the influence propagation. However, a significant downside has emerged: influence propagation can unintentionally activate previously isolated but mutually hostile nodes in cyberspace, increasing the likelihood of online conflicts. To mitigate this issue, we propose the Conflict-Aware Influence Maximization (CAIM) problem, which seeks to maximize influence while minimizing the activation of mutually hostile nodes on hostile-labeled social networks. We demonstrate that CAIM remains NP-hard and #P-hard, making it a complex non-submodular optimization problem. To overcome this challenge, we propose an efficient estimation method for the objective function and design a convergent algorithm with a data-driven approximation of <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10115_2025_2466_Article_IEq1.gif" Format="GIF" Height="21" Rendition="HTML" Resolution="72" Type="Linedraw" Width="45" /> </InlineMediaObject> <EquationSource Format="TEX">\(z_\lambda ^+ / b^+\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msubsup> <mi>z</mi> <mi>λ</mi> <mo>+</mo> </msubsup> <mo stretchy="false">/</mo> <msup> <mi>b</mi> <mo>+</mo> </msup> </mrow> </math></EquationSource> </InlineEquation> , where the parameter computations are intricately connected to the solution. Experiments on real-world networks demonstrate that our algorithms outperform multiple baselines in effectively preventing conflicts while maximizing influence.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Conflict-aware influence maximization on hostile-labeled social networks

  • 期刊论文

  • 发表日期：

    2025年5月20日

查看全文
  • Guoyao Rao,
  • Deying Li,
  • Yuqing Zhu

摘要

Influence maximization (IM) is a well-known problem in social network analysis, which aims to identify a strategic set of seeds to maximize the influence propagation. However, a significant downside has emerged: influence propagation can unintentionally activate previously isolated but mutually hostile nodes in cyberspace, increasing the likelihood of online conflicts. To mitigate this issue, we propose the Conflict-Aware Influence Maximization (CAIM) problem, which seeks to maximize influence while minimizing the activation of mutually hostile nodes on hostile-labeled social networks. We demonstrate that CAIM remains NP-hard and #P-hard, making it a complex non-submodular optimization problem. To overcome this challenge, we propose an efficient estimation method for the objective function and design a convergent algorithm with a data-driven approximation of \(z_\lambda ^+ / b^+\) z λ + / b + , where the parameter computations are intricately connected to the solution. Experiments on real-world networks demonstrate that our algorithms outperform multiple baselines in effectively preventing conflicts while maximizing influence.