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Cross-View Graph Consistency Learning for Invariant Graph Representations
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Proposes CGCL, a self-supervised graph autoencoder that splits the observed edges into two complementary views and trains each view to reconstruct the other, for invariant representations used in link prediction.
Problem
Learning invariant graph representations for link prediction from an incomplete graph structure, where standard augmentations (edge dropping, node dropping, attribute masking) risk losing information.
Approach
A coupled graph structure augmentation splits observed edges into two complementary views without feature augmentation; a cross-view training scheme reconstructs each view's adjacency from the other view's structure and features, with a GCN encoder and inner-product plus MLP decoder, trained with binary cross-entropy.
Key results
- CGCL reports AUC 97.00 on Cora and 98.48 on PubMed at 10% test ratio, the best in the table, vs. MGAE 96.74 and 97.62.
- CGCL achieves AUC 98.88 on Photo and 98.41 on Computers at 10% test ratio, exceeding the strongest baseline MGAE (98.64 and 98.27).
- The one-view ablation (DGCL one-view) is consistently below full CGCL, e.g., 96.77 vs 97.00 AUC on Cora at 10%.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CGCL instead of MGAE | Link prediction · Cora, 10% test | AUC: 96.74 $\pm$0.09 -> 97.00$\pm$0.15 | Helped |
| Use CGCL instead of MGAE | Link prediction · CiteSeer, 10% test | AUC: 97.62$\pm$0.13 -> 97.35$\pm$0.23 | Hurt |
| Use CGCL instead of MGAE | Link prediction · PubMed, 10% test | AUC: 97.62$\pm$0.02 -> 98.48$\pm$0.03 | Helped |
| Use CGCL instead of MGAE | Link prediction · Photo, 10% test | AUC: 98.64$\pm$0.01 -> 98.88$\pm$0.01 | Helped |
| Use CGCL instead of MGAE | Link prediction · Computers, 10% test | AUC: 98.27$\pm$0.03 -> 98.41$\pm$0.04 | Helped |
| Add the second complementary augmented view (rather than training on one view) (ablation) | CGCL link prediction · Cora, 10% test | AUC: 96.77$\pm$0.13 -> 97.00$\pm$0.15 | Helped |
| Add the second complementary augmented view (rather than training on one view) (ablation) | CGCL link prediction · CiteSeer, 10% test | AUC: 97.10$\pm$0.17 -> 97.26$\pm$0.23 | Helped |
| Add the second complementary augmented view (rather than training on one view) (ablation) | CGCL link prediction · PubMed, 10% test | AUC: 98.30$\pm$0.03 -> 98.48$\pm$0.03 | Helped |
| Add the second complementary augmented view (rather than training on one view) (ablation) | CGCL link prediction · Photo, 10% test | AUC: 98.63$\pm$0.04 -> 98.88$\pm$0.01 | Helped |
| Add the second complementary augmented view (rather than training on one view) (ablation) | CGCL link prediction · Computers, 10% test | AUC: 98.08$\pm$0.03 -> 98.39$\pm$0.04 | Helped |
Limitations
- Theoretical analysis is loose: the bounds on C(a1,a2) and the lemma are stated with sigmoid-range arguments and the lemma proof is omitted.
- Reported gains over MGAE are small (often under 1 AUC point) and CGCL is not best on CiteSeer AP in some settings.
- Evaluated only on five citation and Amazon datasets for link prediction; no node classification or larger graphs.
Experimental setup
Datasets: Cora CiteSeer PubMed Amazon Photo Amazon Computers
Benchmarks: Cora CiteSeer PubMed Photo Computers
Metrics: AUC AP
Baselines: GraphSAGE VGAE SEAL S3GRL S2GAE MGAE
Builds on: VGAE MGAE S2GAE GraphSAGE
Closest papers by meaning
- Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks (NeurIPS 2024 · similarity 0.87)
- Multiplex Graph Representation Learning with Homophily and Consistency (AAAI 2025 · similarity 0.86)
- Graph Contrastive Invariant Learning from the Causal Perspective (AAAI 2024 · similarity 0.86)
- Dissecting the Failure of Invariant Learning on Graphs (NeurIPS 2024 · similarity 0.86)
- Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-Based Similarity (AAAI 2024 · similarity 0.86)
- Community-Invariant Graph Contrastive Learning (ICML 2024 · similarity 0.86)
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