Boundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement

[Submitted on 18 Jun 2026 (v1), last revised 22 Jul 2026 (this version, v2)]

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Abstract:Graph neural networks (GNNs) excel at aggregating neighbor information for classification, yet their performance is hindered by graph structural entanglement, where spurious correlations from semantically irrelevant neighbors contaminate node embeddings. This challenge is most acute for nodes near class boundaries in the embedding space, where amplified structural noise blurs decision boundaries and destabilizes predictions. Existing robust GNN methods largely treat all nodes uniformly, ignoring boundary vulnerabilities. In this paper, to improve classification performance, we tackle graph structural disentanglement by identifying boundary-region entanglement as the primary bottleneck and propose Boundary Embedding Shaping (BES), an adaptive contrastive learning GNN plug-in module that selectively suppresses spurious structural noise at decision boundaries with minimal model parameter perturbation. Extensive experiments demonstrate that BES consistently improves boundary discrimination and outperforms existing leading methods. Notably, BES boosts GCN performance by an average of 3.3% in node classification (up to 5.0% on WikiCS) and achieves superior accuracy in link prediction.

Submission history

From: Zidu Yin [view email]
[v1] Thu, 18 Jun 2026 14:28:10 UTC (12,588 KB)
[v2] Wed, 22 Jul 2026 04:59:00 UTC (12,585 KB)