从传播到保护：用于符号社交网络中危害最小化的风险感知扩散模型
From Propagation to Protection: Risk-Aware Diffusion for Harm Minimization in Signed Social Networks
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中文总结 AI 辅助
针对符号社交网络中敌对关系传播的危害问题，提出风险感知扩散模型RASH，构建危害最小化模型HM，在六个网络上实现最高危害减少。
中文摘要 AI 辅助
现实世界的社会关系并非完全支持性的，通过敌对关系传播的信息可能会增加阻力、焦虑或错误信息，而非被采纳。独立级联模型、线性阈值模型等经典模型，以及从有限种子集最大化传播的影响力最大化（IM），均将激活视为离散且不可逆的。其对应的影响力最小化（Inf-Min）虽限制不良传播，但同样依赖简化的激活假设。符号扩展模型虽纳入了极性，但大多保留了这种不可逆性，使得个体在竞争影响下的信念无法减弱、逆转或恢复。此外，两种目标通常将个体同等对待，未考虑脆弱性差异，也未优先保护风险最高的个体。我们提出RASH，一种符号化、易感性感知的扩散模型，其中节点的感知是连续、有界且非单调的；并证明尽管表达能力增强，当仅存在正边或负边时，该模型仍保持单调性和γ-弱次模性，在严格次模性被证明失效的情况下，保留了可处理的贪心近似保证。基于RASH，我们构建了危害最小化（HM），该模型在最大化总感知范围的同时最小化感知缺口（危害）。我们证明HM是NP难的，但其危害减少公式继承了相同的单调性和弱次模性结构，允许具有有界近似比的贪心算法。在六个结构各异的符号网络中，据我们所知，RASH是所测试的唯一能让感知在激活后逆转的扩散模型，使持续的负面影响驱动感知从正向转向负向；而HM在所有评估方法中实现了最高的危害减少，包括其自身的边界情况（IM和Inf-Min）。
英文摘要
Real-world social relationships are not uniformly supportive. Information through hostile connections can increase resistance, anxiety, or misinformation rather than adoption. Classical models such as Independent Cascade and Linear Threshold, together with Influence Maximization (IM), which maximizes spread from a limited seed set, treat activation as discrete and irreversible. Its counterpart, Influence Minimization (Inf-Min), limits undesirable spread but similarly relies on simplified activation assumptions. Signed extensions incorporate polarity but largely retain this irreversibility, leaving no room for beliefs to weaken, reverse, or recover under competing influence. Moreover, both objectives typically treat individuals uniformly, without accounting for differences in vulnerability or prioritizing protection of those most at risk. We introduce RASH, a signed, susceptibility-aware diffusion model in which node awareness is continuous, bounded, and non-monotonic, and prove that despite this added expressiveness it remains monotone and γ-weakly submodular where only positive or negative edges exist, preserving tractable greedy approximation guarantees where strict submodularity provably fails. Building on RASH, we formulate Harm Minimization (HM), which maximizes aggregate reach while minimizing the awareness shortfall (harm). We prove HM is NP-hard, yet its harm-reduction formulation inherits the same monotonicity and weak-submodularity structure, admitting a greedy algorithm with a bounded approximation ratio. Across six structurally diverse signed networks, RASH is the only diffusion model tested to our knowledge that ever allows awareness to reverse after activation, letting sustained discouraging influence drive awareness from positive toward negative, and HM achieves the highest harm reduction of any method evaluated, including its own boundary cases (IM and Inf-Min)
