Fair Graph Mining

In today's increasingly connected world, graph mining plays a pivotal role in many real-world application domains, including social network analysis, recommendations, marketing and financial security. 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) algorithmic 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, individual 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 intelligence, social science and beneficial to a plethora of real-world application domains.