Influence Blocking Maximization (IBM) aims to select a positive seed set to suppress the spread of negative influence. However, existing IBM methods focus solely on maximizing blocking effectiveness, overlooking fairness across communities. To address this issue, we formalize fairness in IBM and justify Demographic Parity (DP) as a notion that is particularly well aligned with its semantics. Yet enforcing DP is computationally challenging: prior work typically formulates DP as a Linear Programming (LP) problem and relies on costly solvers, rendering them impractical for large-scale networks. In this paper, we propose a DP-aware objective while maintaining an approximately monotonic submodular structure, enabling efficient optimization with theoretical guarantees. We integrate this objective with blocking effectiveness through a tunable scalarization, yielding a principled fairness-effectiveness trade-offs. Building on this structure, we develop CELF-R, an accelerated seed selection algorithm that exploits approximate submodularity to eliminate redundant evaluations and naturally supports Pareto front construction. Extensive experiments demonstrate that CELF-R consistently outperforms state-of-the-art baselines, achieving a $(1-1/e-ψ)$-approximate solution while maintaining high efficiency.

翻译：影响力阻断最大化（IBM）旨在通过选取正向种子集来抑制负面影响的传播。然而，现有的IBM方法仅关注最大化阻断效果，忽视了不同社群间的公平性。为解决这一问题，我们形式化了IBM中的公平性概念，并论证了人口统计均等（DP）作为一种与IBM语义高度契合的公平性度量。然而，强制实施DP在计算上具有挑战性：先前研究通常将DP表述为线性规划（LP）问题并依赖昂贵的求解器，导致其难以应用于大规模网络。本文提出了一种在保持近似单调次模结构的同时融入DP感知的目标函数，从而在理论保证下实现高效优化。我们通过可调标量化将该目标与阻断效果相结合，形成了具有理论依据的公平性-效果权衡框架。基于此结构，我们开发了CELF-R算法——一种利用近似次模性消除冗余评估的加速种子选择算法，该算法天然支持帕累托前沿构建。大量实验表明，CELF-R在保持高效率的同时，始终优于现有基线方法，能够实现$(1-1/e-ψ)$近似解。