{"total":26,"rows":[{"id":"2106.06134","title":"Is Homophily a Necessity for Graph Neural Networks?","created":"2021-06-11","cited_by_count":182,"area":null},{"id":"2102.05034","title":"SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks","created":"2021-02-09","cited_by_count":110,"area":null},{"id":"2108.01099","title":"Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data","created":"2021-08-02","cited_by_count":87,"area":null},{"id":"2104.04883","title":"Graph Representation Learning in Biomedicine","created":"2021-04-11","cited_by_count":80,"area":null},{"id":"2210.03561","title":"Empowering Graph Representation Learning with Test-Time Graph Transformation","created":"2022-10-07","cited_by_count":67,"area":null},{"id":"2406.10727","title":"Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights","created":"2024-06-15","cited_by_count":44,"area":null},{"id":"2105.04528","title":"Accelerating Large Scale Real-Time GNN Inference using Channel Pruning","created":"2021-05-10","cited_by_count":41,"area":null},{"id":"2306.01323","title":"Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?","created":"2023-06-02","cited_by_count":41,"area":null},{"id":"2309.16595","title":"Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?","created":"2023-09-28","cited_by_count":31,"area":null},{"id":"2402.11139","title":"LiGNN: Graph Neural Networks at LinkedIn","created":"2024-02-17","cited_by_count":31,"area":null},{"id":"2211.06614","title":"Robust Training of Graph Neural Networks via Noise Governance","created":"2022-11-12","cited_by_count":29,"area":null},{"id":"2305.18457","title":"Learning Strong Graph Neural Networks with Weak Information","created":"2023-05-29","cited_by_count":25,"area":null},{"id":"2207.03522","title":"TF-GNN: Graph Neural Networks in TensorFlow","created":"2022-07-07","cited_by_count":21,"area":null},{"id":"2105.09384","title":"Graph Sanitation with Application to Node Classification","created":"2021-05-19","cited_by_count":20,"area":null},{"id":"2204.01376","title":"Synthetic Graph Generation to Benchmark Graph Learning","created":"2022-04-04","cited_by_count":16,"area":null},{"id":"2310.07684","title":"Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design","created":"2023-10-11","cited_by_count":15,"area":null},{"id":"2403.07478","title":"Towards Graph Foundation Models for Personalization","created":"2024-03-12","cited_by_count":12,"area":null},{"id":"2409.17475","title":"On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks","created":"2024-09-26","cited_by_count":10,"area":null},{"id":"2308.10737","title":"UGSL: A Unified Framework for Benchmarking Graph Structure Learning","created":"2023-08-21","cited_by_count":9,"area":null},{"id":"2304.07210","title":"Measuring Re-identification Risk","created":"2023-04-12","cited_by_count":6,"area":null},{"id":"2307.02126","title":"Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix","created":"2023-07-05","cited_by_count":2,"area":null},{"id":"2212.02635","title":"Stars: Tera-Scale Graph Building for Clustering and Graph Learning","created":"2022-12-05","cited_by_count":1,"area":null},{"id":"2310.07667","title":"Global Minima, Recoverability Thresholds, and Higher-Order Structure in GNNS","created":"2023-10-11","cited_by_count":0,"area":null},{"id":"2507.10139","title":"Large-Scale Graph Building in Dynamic Environments: Low Latency and High Quality","created":"2025-07-14","cited_by_count":0,"area":null},{"id":"2508.02609","title":"Entity Representation Learning Through Onsite-Offsite Graph for Pinterest Ads","created":"2025-08-04","cited_by_count":0,"area":null},{"id":"2508.09544","title":"SYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification","created":"2025-08-13","cited_by_count":0,"area":null}],"next_cursor":null}