Scalable Fair Influence Blocking Maximization via Approximately Monotonic Submodular Optimization

TL;DR

This paper introduces a scalable, fair influence blocking method that balances effectiveness and demographic fairness in large networks using approximately monotonic submodular optimization.

Contribution

It formalizes fairness in influence blocking, proposes a computationally efficient optimization framework, and develops an accelerated algorithm with theoretical guarantees.

Findings

CELF-R outperforms existing methods in efficiency and effectiveness.

Achieves near-optimal solutions with theoretical approximation guarantees.

Supports Pareto front construction for fairness-effectiveness trade-offs.

Abstract

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 $(1-1/e-\psi)$-approximate solution while maintaining high efficiency.