Mini-Batch Class Composition Bias in Link Prediction

• URL: http://arxiv.org/abs/2604.25978v1
• Date: Tue, 28 Apr 2026 14:03:50 GMT
• Title: Mini-Batch Class Composition Bias in Link Prediction
• Abstract summary: We find popular link prediction models can learn a trivial mini-batch dependent to solve the edge classification task.<n>When correcting for this, we observe increased alignment of the network representation with node-class relevant features.<n>Our findings suggest that standard link prediction training may be leading us to overestimate link predictors' ability to learn a generalised representation of a graph.
• Score: 7.131835212307929
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prior work on node classification has shown that Graph Neural Networks (GNNs) can learn representations that transfer across graphs, when underlying graph properties are shared. For a fixed graph, one would then expect GNNs trained for link prediction to learn a representation consistent with that learnt for node classification. We show this intuition does not hold in the general case. Instead, we find popular link prediction models can learn a trivial mini-batch dependent heuristic, enabled by batch-normalisation layers, to solve the edge classification task. When correcting for this, we observe increased alignment of the network representation with node-class relevant features, suggesting the network has learnt a graph representation that better aligns with the underlying graph's properties. Our findings suggest that standard link prediction training may be leading us to overestimate link predictors' ability to learn a generalised representation of a graph that is consistent across tasks.

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