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Fair Link Prediction With Overlapping Groups
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DOI：10.1109/TCSS.2024.3479702.png)
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Abstract
En 中文
In this article, we introduce FairLPG, a framework for ensuring fairness for the task of link prediction in graphs with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple</i> sensitive attributes. In the context of link prediction in graphs, the fairness notions of demographic parity and equalized odds try to ensure equal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">average linking probability</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">true positive rates</i> across different demographic groups consisting of various node pairs. Existing methods for achieving fairness in link prediction only consider a single sensitive attribute, which makes them unsuited for applications where multiple sensitive attributes need to be accounted for. Additionally, considering multiple sensitive attributes in the context of link prediction leads to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">overlapping</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intersectional</i> groups, which further complicates designing such a framework. The proposed framework FairLPG assumes that the link prediction model generates a prediction score for each node pair to form an edge, and formulates a convex optimization problem that minimizes the squared Euclidean distance between the original prediction scores and transformed scores, subject to the fairness constraints. The transformed scores are then utilized for fair link prediction. To the best of our knowledge, this work is the first to handle the case of intersectional sensitive groups in the graph setting. To demonstrate its effectiveness, we deploy FairLPG on several real-world datasets and graph neural network based link prediction models. It either outperforms or performs competitively with existing methods both in terms of fairness and prediction accuracy across all the datasets and link prediction models at the same time being computationally more efficient.
Keywords:
Convex optimization
fairness in graphs
link prediction
score transformation
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