Addressing the Impact of Localized Training Data in Graph Neural Networks

In the realm of Graph Neural Networks (GNNs), which excel at capturing intricate dependencies in graph-structured data, we address a significant limitation. Most state-of-the-art GNNs assume an in-distribution setting, limiting their performance on real-world dynamic graphs. This article seeks to assess the influence of training GNNs using localized subsets of graphs. Such constrained training data could result in a model performing well in the specific trained region, but struggling to generalize and provide accurate predictions for the entire graph. Within the realm of graph-based semi-supervised learning (SSL), resource constraints frequently give rise to situations where a substantial dataset is available, yet only a fraction of it can be labeled. Such circumstances directly impact the model’s efficacy. The limitations stemming from localized training data significantly impact tasks such as anomaly and spam detection, especially when labeling processes are biased or influenced by human subjectivity. To address these challenges, we frame the issue as an out-of-distribution (OOD) data problem. This involves aligning the distributions between the training data, which constitutes a small fraction of labeled data, and the graph inference process responsible for predicting the entire graph. To mitigate these challenges, we introduce a regularization method designed to minimize distributional discrepancies between 10-calized training data and the graph inference process. This regularization technique enhances the model’s performance on OOD data. Rigorous testing on well-established Graph Neural Network (GNN) models demonstrates significant performance improvements across three citation GNN benchmark datasets. The regularization approach not only improves model adaptation but also enhances generalization, effectively overcoming the challenges posed by OOD data.
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