Mini-Batch Class Composition Bias in Link Prediction
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, suggesti...
| Authors | Kieran Maguire, Srinandan Dasmahapatra |
|---|---|
| Primary category | cs.LG |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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