Maximizing fair content spread via edge suggestion in social networks

Abstract

Content spread inequity is a potential unfairness issue in online social networks, disparately impacting minority groups. In this paper, we view friendship suggestion, a common feature in social network platforms, as an opportunity to achieve an equitable spread of content. In particular, we propose to suggest a subset of potential edges (currently not existing in the network but likely to be accepted) that maximizes content spread while achieving fairness. Instead of re-engineering the existing systems, our proposal builds a fairness wrapper on top of the existing friendship suggestion components. We prove the problem is NP-hard and inapproximable in polynomial time unless P = NP. Therefore, allowing relaxation of the fairness constraint, we propose an algorithm based on LP-relaxation and randomized rounding with fixed approximation ratios on fairness and content spread. We provide multiple optimizations, further improving the performance of our algorithm in practice. Besides, we propose a scalable algorithm that dynamically adds subsets of nodes, chosen via iterative sampling, and solves smaller problems corresponding to these nodes. Besides theoretical analysis, we conduct comprehensive experiments on real and synthetic data sets. Across different settings, our algorithms found solutions with near-zero unfairness while significantly increasing the content spread. Our scalable algorithm could process a graph with half a million nodes on a single machine, reducing the unfairness to around 0.0004 while lifting content spread by 43%.

Fair Influence Maximization in Large-scale Social Networks Based on Attribute-aware Reverse Influence Sampling

“…• Third, besides the fair influence maximization problem discussed in this work, there are also fairness in budgets (Nguyen, Pham, Le, & Snášel, 2022), fairness of time (Ali et al, 2022) and fairness of content spread (Swift, Ebrahimi, Nova, & Asudeh, 2022) for the influence maximization problem. Therefore, more variants of fairness can be taken into consideration to meet the tangible needs.…”

Improving Fairness in Information Exposure by Adding Links

“…Khalil, Dilkina, and Song (2014) study both the edge addition and deletion problems in order to maximize/minimize influence in the linear threshold model. Swift et al (2022) introduce a problem to suggest a set of edges that contains at most k edges incident to each node to maximize the expected number of reached nodes while satisfying a fairness constraint (reaching each group in the network with the same probability). Garimella et al (2017) address the problem of recommending a set of edges to minimize the controversy score of the graph.…”