【CIKM2021-Tutorial】图挖掘公平性，166页ppt

2021 年 11 月 5 日 专知

在当今日益互联的世界，图挖掘在许多现实世界的应用领域发挥着关键作用，包括社交网络分析、建议、营销和金融安全。人们作出了巨大的努力来发展广泛的计算模型。然而，最近的研究表明，许多被广泛应用的图挖掘模型可能会受到潜在的歧视。图挖掘的公平性旨在制定策略以减少挖掘过程中引入或放大的偏差。在图挖掘中加强公平性的独特挑战包括: (1)图数据的非iid性质的理论挑战，这可能会使许多现有研究背后的公平机器学习的基本假设无效，(2) 算法挑战平衡模型准确性和公平性的困境。本教程旨在(1)全面回顾图挖掘方面最先进的技术，(2)确定有待解决的挑战和未来的趋势。特别是，我们首先回顾了背景、问题定义、独特的挑战和相关问题;然后，我们将重点深入概述(1)在图挖掘背景下实施群体公平、个人公平和其他公平概念的最新技术，以及(2)图上算法公平的未来研究方向。我们相信，本教程对数据挖掘、人工智能、社会科学等领域的研究人员和实践者具有吸引力，并对现实世界的众多应用领域有益。

http://jiank2.web.illinois.edu/tutorial/cikm21/fair_graph_mining.html

目录内容：

引言 Introduction

Background and motivations
Problem definitions and settings
Key challenges
Related problems

群组公平性 Part I: Group Fairness on Graphs

Fair graph ranking
Fair graph clustering
Fair graph embedding

个体公平性 Part II: Individual Fairness on Graphs

Optimization-based method
Ranking-based method

Part III: Other Fairness Definitions on Graphs

Rawlsian fairness
Degree-related fairness
Counterfactual fairness

开放挑战与未来方向 Part IV: Open Challenges and Future Directions

Fairness on dynamic graphs
Fairness on multi-network mining
Multi-resolution fairness on graphs
Connections between group fairness and individual fairness on graphs

讲者：

参考文献：

Tsioutsiouliklis, S., Pitoura, E., Tsaparas, P., Kleftakis, I., & Mamoulis, N. (2021). Fairness-Aware PageRank. In Proceedings of the Web Conference 2021 (pp. 3815-3826).
Kleindessner, M., Samadi, S., Awasthi, P., & Morgenstern, J. (2019). Guarantees for Spectral Clustering with Fairness Constraints. In International Conference on Machine Learning (pp. 3458-3467).
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Rahmattalabi, A., Vayanos, P., Fulginiti, A., Rice, E., Wilder, B., Yadav, A., & Tambe, M. (2019). Exploring Algorithmic Fairness in Robust Graph Covering Problems. In Advances in Neural Information Processing Systems, (pp. 15776-15787).
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Agarwal, C., Lakkaraju, H., & Zitnik, M. (2021). Towards a Unified Framework for Fair and Stable Graph Representation Learning. In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence.