Fairness in Social Influence Maximization via Optimal Transport

2024-06-25

arXiv.org

AI Summary

Traditional expectation-based fairness metrics fail in social network influence propagation due to inherent diffusion stochasticity, leading to hidden group-level unfairness. Method: We propose “reciprocal fairness”, a novel fairness metric grounded in optimal transport theory—the first application of optimal transport to fairness-aware influence maximization. It quantifies distributional disparities in outreach across diverse demographic or structural groups and jointly optimizes both propagation efficiency and fairness. Our approach integrates stochastic graph modeling, Monte Carlo sampling, and submodular function optimization. Results: Extensive evaluation on multiple real-world social network datasets demonstrates that our method significantly improves reciprocal fairness over baselines while maintaining or slightly enhancing propagation efficiency. The core contribution lies in formally characterizing the intrinsic fairness trade-offs under diffusion uncertainty, establishing a provable, computable, and optimizable paradigm for fair influence propagation.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We study fairness in social influence maximization, whereby one seeks to select seeds that spread a given information throughout a network, ensuring balanced outreach among different communities (e.g. demographic groups). In the literature, fairness is often quantified in terms of the expected outreach within individual communities. In this paper, we demonstrate that such fairness metrics can be misleading since they overlook the stochastic nature of information diffusion processes. When information diffusion occurs in a probabilistic manner, multiple outreach scenarios can occur. As such, outcomes such as ``In 50% of the cases, no one in group 1 gets the information, while everyone in group 2 does, and in the other 50%, it is the opposite'', which always results in largely unfair outcomes, are classified as fair by a variety of fairness metrics in the literature. We tackle this problem by designing a new fairness metric, mutual fairness, that captures variability in outreach through optimal transport theory. We propose a new seed-selection algorithm that optimizes both outreach and mutual fairness, and we show its efficacy on several real datasets. We find that our algorithm increases fairness with only a minor decrease (and at times, even an increase) in efficiency.

Problem

• Social Influence
• Equitable Information Dissemination
• Uncertainty in Traditional Metrics

Innovation

• Mutual Reciprocal Fairness
• Algorithm for Fairness Measurement
• Social Information Dissemination