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1. Fair Graph Mining

   https://doi.org/10.1145/3459637.3482030  

   Kang, Jian; Tong, Hanghang (October 2021, CIKM)

   In today’s increasingly connected world, graph mining plays a piv- otal role in many real-world application domains, including social network analysis, recommendations, marketing and financial secu- rity. Tremendous efforts have been made to develop a wide range of computational models. However, recent studies have revealed that many widely-applied graph mining models could suffer from potential discrimination. Fairness on graph mining aims to develop strategies in order to mitigate bias introduced/amplified during the mining process. The unique challenges of enforcing fairness on graph mining include (1) theoretical challenge on non-IID nature of graph data, which may invalidate the basic assumption behind many existing studies in fair machine learning, and (2) algorith- mic challenge on the dilemma of balancing model accuracy and fairness. This tutorial aims to (1) present a comprehensive review of state-of-the-art techniques in fairness on graph mining and (2) identify the open challenges and future trends. In particular, we start with reviewing the background, problem definitions, unique challenges and related problems; then we will focus on an in-depth overview of (1) recent techniques in enforcing group fairness, indi- vidual fairness and other fairness notions in the context of graph mining, and (2) future directions in studying algorithmic fairness on graphs. We believe this tutorial could be attractive to researchers and practitioners in areas including data mining, artificial intel- ligence, social science and beneficial to a plethora of real-world application domains. 

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2. Fairness in Graph Mining: A Survey

   https://doi.org/10.1109/TKDE.2023.3265598  

   Dong, Yushun; Ma, Jing; Wang, Song; Chen, Chen; Li, Jundong (January 2023, IEEE Transactions on Knowledge and Data Engineering)

   Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to discrimination towards certain populations when exploited in human-centered applications. Recently, algorithmic fairness has been extensively studied in graph-based applications. In contrast to algorithmic fairness on independent and identically distributed (i.i.d.) data, fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling techniques. In this survey, we provide a comprehensive and up-to-date introduction of existing literature under the context of fair graph mining. Specifically, we propose a novel taxonomy of fairness notions on graphs, which sheds light on their connections and differences. We further present an organized summary of existing techniques that promote fairness in graph mining. Finally, we discuss current research challenges and open questions, aiming at encouraging cross-breeding ideas and further advances. 

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3. InFoRM: Individual Fairness on Graph Mining

   https://doi.org/doi.org/10.1145/3394486.3403080  

   Jian Kang, Jingrui He (August 2020, KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data)

   null (Ed.)

   Algorithmic bias and fairness in the context of graph mining have largely remained nascent. The sparse literature on fair graph mining has almost exclusively focused on group-based fairness notation. However, the notion of individual fairness, which promises the fairness notion at a much finer granularity, has not been well studied. This paper presents the first principled study of Individual Fairness on gRaph Mining (InFoRM). First, we present a generic definition of individual fairness for graph mining which naturally leads to a quantitative measure of the potential bias in graph mining results. Second, we propose three mutually complementary algorithmic frameworks to mitigate the proposed individual bias measure, namely debiasing the input graph, debiasing the mining model and debiasing the mining results. Each algorithmic framework is formulated from the optimization perspective, using effective and efficient solvers, which are applicable to multiple graph mining tasks. Third, accommodating individual fairness is likely to change the original graph mining results without the fairness consideration. We conduct a thorough analysis to develop an upper bound to characterize the cost (i.e., the difference between the graph mining results with and without the fairness consideration). We perform extensive experimental evaluations on real-world datasets to demonstrate the efficacy and generality of the proposed methods. 

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4. iFiG: Individually Fair Multi-view Graph Clustering

   https://doi.org/10.1109/BigData55660.2022.10020548  

   Wang, Yian; Kang, Jian; Xia, Yinglong; Luo, Jiebo; Tong, Hanghang (December 2022, 2022 IEEE International Conference on Big Data (Big Data))

   In a connected world, fair graph learning is becoming increasingly important because of the growing concerns about bias. Yet, the vast majority of existing works assume that the input graph comes from a single view while ignoring the multi-view essence of graphs. Generally speaking, the bias in graph mining is often rooted in the input graph and is further introduced or even amplified by the graph mining model. It thus poses critical research questions regarding the intrinsic relationships of fairness on different views and the possibility of mitigating bias on multiple views simultaneously. To answer these questions, in this paper, we explore individual fairness in multi-view graph mining. We first demonstrate the necessity of fair multi-view graph learning. Building upon the optimization perspective of fair single-view graph mining, we then formulate our problem as a linear weighted optimization problem. In order to figure out the weight of each view, we resort to the minimax Pareto fairness, which is closely related to the Rawlsian difference principle, and propose an effective solver named iFiG that minimizes the utility loss while promoting individual fairness for each view with two different instantiations. The extensive experiments that we conduct in the application of multi-view spectral clustering and INFORM post-processing demonstrate the efficacy of our proposed method in individual bias mitigation. 

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5. Learning Fair Node Representations with Graph Counterfactual Fairness

   https://doi.org/10.1145/3488560.3498391  

   Ma, Jing; Guo, Ruocheng; Wan, Mengting; Yang, Longqi; Zhang, Aidong; Li, Jundong (February 2022, Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining)

   Fair machine learning aims to mitigate the biases of model predictions against certain subpopulations regarding sensitive attributes such as race and gender. Among the many existing fairness notions, counterfactual fairness measures the model fairness from a causal perspective by comparing the predictions of each individual from the original data and the counterfactuals. In counterfactuals, the sensitive attribute values of this individual had been modified. Recently, a few works extend counterfactual fairness to graph data, but most of them neglect the following facts that can lead to biases: 1) the sensitive attributes of each node's neighbors may causally affect the prediction w.r.t. this node; 2) the sensitive attributes may causally affect other features and the graph structure. To tackle these issues, in this paper, we propose a novel fairness notion - graph counterfactual fairness, which considers the biases led by the above facts. To learn node representations towards graph counterfactual fairness, we propose a novel framework based on counterfactual data augmentation. In this framework, we generate counterfactuals corresponding to perturbations on each node's and their neighbors' sensitive attributes. Then we enforce fairness by minimizing the discrepancy between the representations learned from the original graph and the counterfactuals for each node. Experiments on both synthetic and real-world graphs show that our framework outperforms the state-of-the-art baselines in graph counterfactual fairness, and also achieves comparable prediction performance. 

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