From Propagation to Protection: Risk-Aware Diffusion for Harm Minimization in Signed Social Networks

2026-08-21

AI Summary

为减少有符号社交网络中的负面影响，提出RASH模型以考虑节点的连续、有界且非单调意识，并通过Harm Minimization方法最大化覆盖同时最小化伤害。

Abstract

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)

Problem

• signed social networks
• harm minimization
• influence maximization
• vulnerability
• awareness

Innovation

• RASH
• Harm Minimization
• Non-monotonic Awareness
• Weak Submodularity
• Signed Social Networks