Mini-Batch Class Composition Bias in Link Prediction

📰 ArXiv cs.AI

Learn how mini-batch class composition bias affects link prediction in Graph Neural Networks (GNNs) and how to address it

advanced Published 30 Apr 2026

Action Steps

Identify the mini-batch class composition bias in link prediction models using GNNs
Analyze the effect of this bias on the learned representations
Apply techniques to mitigate the bias, such as batch normalization or weighted sampling
Evaluate the performance of link prediction models with and without bias mitigation
Compare the results to understand the impact of the bias on model accuracy

Who Needs to Know This

Data scientists and researchers working on graph-based machine learning models can benefit from understanding this bias and its implications for link prediction tasks

Key Insight

💡 Mini-batch class composition bias can significantly affect the performance of link prediction models in GNNs

Key Takeaways

Learn how mini-batch class composition bias affects link prediction in Graph Neural Networks (GNNs) and how to address it

Full Article

Title: Mini-Batch Class Composition Bias in Link Prediction

Abstract:
arXiv:2604.25978v1 Announce Type: cross 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 t

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