{"total":23,"rows":[{"id":"2405.00476","title":"A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges","created":"2024-05-01","cited_by_count":30,"area":null},{"id":"2410.19265","title":"A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation","created":"2024-10-25","cited_by_count":17,"area":null},{"id":"2511.05592","title":"GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuning","created":"2025-11-05","cited_by_count":13,"area":null},{"id":"2408.00490","title":"Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation","created":"2024-08-01","cited_by_count":11,"area":null},{"id":"2408.04400","title":"DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization","created":"2024-08-08","cited_by_count":7,"area":null},{"id":"2402.06716","title":"Dynamic Graph Information Bottleneck","created":"2024-02-09","cited_by_count":6,"area":null},{"id":"2505.24178","title":"Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem","created":"2025-05-30","cited_by_count":5,"area":null},{"id":"2407.19183","title":"Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks","created":"2024-07-27","cited_by_count":4,"area":null},{"id":"2412.15735","title":"Prompt-based Unifying Inference Attack on Graph Neural Networks","created":"2024-12-20","cited_by_count":3,"area":null},{"id":"2511.02354","title":"Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments","created":"2025-11-04","cited_by_count":2,"area":null},{"id":"2407.10204","title":"Improving Graph Out-of-distribution Generalization Beyond Causality","created":"2024-07-14","cited_by_count":1,"area":null},{"id":"2504.00328","title":"Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts","created":"2025-04-01","cited_by_count":1,"area":null},{"id":"2505.16080","title":"SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation","created":"2025-05-21","cited_by_count":1,"area":null},{"id":"2509.23816","title":"Test-time GNN Model Evaluation on Dynamic Graphs","created":"2025-09-28","cited_by_count":1,"area":null},{"id":"2403.01738","title":"ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution","created":"2024-03-04","cited_by_count":0,"area":null},{"id":"2405.19062","title":"Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts","created":"2024-05-29","cited_by_count":0,"area":null},{"id":"2410.14726","title":"Incorporating Long-term Data in Training Short-term Traffic Prediction Model","created":"2024-10-16","cited_by_count":0,"area":null},{"id":"2501.01707","title":"Catch Causal Signals from Edges for Label Imbalance in Graph Classification","created":"2025-01-03","cited_by_count":0,"area":null},{"id":"2504.15613","title":"Learning Dynamic Graphs via Tensorized and Lightweight Graph Convolutional Networks","created":"2025-04-22","cited_by_count":0,"area":null},{"id":"2505.06283","title":"Soft causal learning for generalized molecule property prediction: An environment perspective","created":"2025-05-07","cited_by_count":0,"area":null},{"id":"2506.03674","title":"Out-of-Distribution Graph Models Merging","created":"2025-06-04","cited_by_count":0,"area":null},{"id":"2608.03647","title":"Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2608.27948","title":"Temporal Memory-Aware Online Test-Time Adaptation on Dynamic Graphs","created":"2026-08-28","cited_by_count":0,"area":null}],"next_cursor":null}