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CrossWalk: Fairness-enhanced Node Representation Learning
Ahmad Khajehnejad · Moein Khajehnejad · Krishna Gummadi · Adrian Weller · Baharan Mirzasoleiman

@
in Workshop on Socially Responsible Machine Learning »

The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. However, there is little work on enhancing fairness in graph algorithms. Here, we develop a simple, effective and general method, CrossWalk, that enhances fairness of various graph algorithms, including influence maximization, link prediction and node classification, applied to node embeddings. CrossWalk is applicable to any random walk based node representation learning algorithm, such as DeepWalk and Node2Vec. The key idea is to bias random walks to cross group boundaries, by upweighting edges which (1) are closer to the groups' peripheries or (2) connect different groups in the network. It pulls nodes that are near groups' peripheries towards their neighbors from other groups in the embedding space, while preserving the necessary structural information from the graph. Extensive experiments show the effectiveness of our algorithm to enhance fairness in various graph algorithms in synthetic and real networks, with only a very small decrease in performance.

Author Information

Ahmad Khajehnejad (Sharif University of Technology)
Moein Khajehnejad (Monash University)
Krishna Gummadi (MPI-SWS)
Adrian Weller (University of Cambridge, Alan Turing Institute)
Adrian Weller

Adrian Weller is Programme Director for AI at The Alan Turing Institute, the UK national institute for data science and AI, and is a Turing AI Fellow leading work on trustworthy Machine Learning (ML). He is a Principal Research Fellow in ML at the University of Cambridge, and at the Leverhulme Centre for the Future of Intelligence where he is Programme Director for Trust and Society. His interests span AI, its commercial applications and helping to ensure beneficial outcomes for society. Previously, Adrian held senior roles in finance. He received a PhD in computer science from Columbia University, and an undergraduate degree in mathematics from Trinity College, Cambridge.

Baharan Mirzasoleiman (Stanford University)

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