{"total":124,"rows":[{"id":"2305.19466","title":"The Impact of Positional Encoding on Length Generalization in Transformers","created":"2023-05-31","cited_by_count":327,"area":null},{"id":"2102.09544","title":"Combinatorial optimization and reasoning with graph neural networks","created":"2021-02-18","cited_by_count":268,"area":null},{"id":"2207.04901","title":"Exploring Length Generalization in Large Language Models","created":"2022-07-11","cited_by_count":206,"area":null},{"id":"2202.02466","title":"Handling Distribution Shifts on Graphs: An Invariance Perspective","created":"2022-02-05","cited_by_count":164,"area":null},{"id":"2202.05441","title":"Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs","created":"2022-02-11","cited_by_count":144,"area":null},{"id":"2209.15240","title":"Universal Prompt Tuning for Graph Neural Networks","created":"2022-09-30","cited_by_count":100,"area":null},{"id":"2202.07987","title":"Out-Of-Distribution Generalization on Graphs: A Survey","created":"2022-02-16","cited_by_count":97,"area":null},{"id":"2310.04562","title":"Towards Foundation Models for Knowledge Graph Reasoning","created":"2023-10-06","cited_by_count":86,"area":null},{"id":"2103.05045","title":"Size-Invariant Graph Representations for Graph Classification Extrapolations","created":"2021-03-08","cited_by_count":84,"area":null},{"id":"2302.04181","title":"Attending to Graph Transformers","created":"2023-02-08","cited_by_count":83,"area":null},{"id":"2112.03806","title":"OOD-GNN: Out-of-Distribution Generalized Graph Neural Network","created":"2021-12-07","cited_by_count":78,"area":null},{"id":"2204.01855","title":"A Survey on Graph Representation Learning Methods","created":"2022-04-04","cited_by_count":66,"area":null},{"id":"2204.07697","title":"Theory of Graph Neural Networks: Representation and Learning","created":"2022-04-16","cited_by_count":61,"area":null},{"id":"2202.00645","title":"Generalization Analysis of Message Passing Neural Networks on Large Random Graphs","created":"2022-02-01","cited_by_count":59,"area":null},{"id":"2209.11142","title":"A Generalist Neural Algorithmic Learner","created":"2022-09-22","cited_by_count":59,"area":null},{"id":"2410.01104","title":"Softmax is not Enough (for Sharp Size Generalisation)","created":"2024-10-01","cited_by_count":46,"area":null},{"id":"2310.19035","title":"Does Invariant Graph Learning via Environment Augmentation Learn Invariance?","created":"2023-10-29","cited_by_count":42,"area":null},{"id":"2210.01906","title":"Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks","created":"2022-10-04","cited_by_count":41,"area":null},{"id":"2305.08048","title":"Towards Understanding the Generalization of Graph Neural Networks","created":"2023-05-14","cited_by_count":41,"area":null},{"id":"2302.04451","title":"Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion","created":"2023-02-09","cited_by_count":40,"area":null},{"id":"2205.15117","title":"OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs","created":"2022-05-30","cited_by_count":38,"area":null},{"id":"2211.02843","title":"Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift","created":"2022-11-05","cited_by_count":38,"area":null},{"id":"2207.07888","title":"SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks","created":"2022-07-16","cited_by_count":37,"area":null},{"id":"2303.14617","title":"Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases","created":"2023-03-26","cited_by_count":35,"area":"lr"},{"id":"2301.11039","title":"WL meet VC","created":"2023-01-26","cited_by_count":31,"area":null},{"id":"2302.10258","title":"Neural Algorithmic Reasoning with Causal Regularisation","created":"2023-02-20","cited_by_count":31,"area":"lr"},{"id":"2402.11494","title":"Graph Out-of-Distribution Generalization via Causal Intervention","created":"2024-02-18","cited_by_count":31,"area":null},{"id":"2502.01612","title":"Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges","created":"2025-02-03","cited_by_count":31,"area":"synth"},{"id":"2211.00692","title":"Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks","created":"2022-11-01","cited_by_count":30,"area":null},{"id":"2306.01103","title":"Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization","created":"2023-06-01","cited_by_count":29,"area":null},{"id":"2308.06714","title":"Learning on Graphs with Out-of-Distribution Nodes","created":"2023-08-13","cited_by_count":29,"area":null},{"id":"2210.08008","title":"Inductive Logical Query Answering in Knowledge Graphs","created":"2022-10-13","cited_by_count":26,"area":null},{"id":"2403.01092","title":"Pairwise Alignment Improves Graph Domain Adaptation","created":"2024-03-02","cited_by_count":25,"area":null},{"id":"2410.03424","title":"Cayley Graph Propagation","created":"2024-10-04","cited_by_count":21,"area":null},{"id":"2411.14001","title":"Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction","created":"2024-11-21","cited_by_count":20,"area":null},{"id":"2201.08459","title":"Federated Learning with Heterogeneous Architectures using Graph HyperNetworks","created":"2022-01-20","cited_by_count":19,"area":null},{"id":"2312.04693","title":"GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts","created":"2023-12-07","cited_by_count":19,"area":"moe"},{"id":"2306.04495","title":"Limits, approximation and size transferability for GNNs on sparse graphs via graphops","created":"2023-06-07","cited_by_count":18,"area":null},{"id":"2312.10988","title":"Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization","created":"2023-12-18","cited_by_count":18,"area":null},{"id":"2212.12070","title":"RouteNet-Fermi: Network Modeling with Graph Neural Networks","created":"2022-12-22","cited_by_count":17,"area":null},{"id":"2310.20082","title":"Efficient Subgraph GNNs by Learning Effective Selection Policies","created":"2023-10-30","cited_by_count":17,"area":null},{"id":"2406.05225","title":"A Manifold Perspective on the Statistical Generalization of Graph Neural Networks","created":"2024-06-07","cited_by_count":17,"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":"2301.10808","title":"Graph Neural Tangent Kernel: Convergence on Large Graphs","created":"2023-01-25","cited_by_count":16,"area":null},{"id":"2306.15902","title":"Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization","created":"2023-06-28","cited_by_count":15,"area":null},{"id":"2306.03256","title":"Explaining and Adapting Graph Conditional Shift","created":"2023-06-05","cited_by_count":14,"area":null},{"id":"2402.07568","title":"Weisfeiler-Leman at the margin: When more expressivity matters","created":"2024-02-12","cited_by_count":13,"area":null},{"id":"2406.07955","title":"How Interpretable Are Interpretable Graph Neural Networks?","created":"2024-06-12","cited_by_count":13,"area":null},{"id":"2506.09251","title":"Extrapolation by Association: Length Generalization Transfer in Transformers","created":"2025-06-10","cited_by_count":13,"area":null},{"id":"2211.15899","title":"FakeEdge: Alleviate Dataset Shift in Link Prediction","created":"2022-11-29","cited_by_count":12,"area":null},{"id":"2503.19173","title":"Graph neural networks extrapolate out-of-distribution for shortest paths","created":"2025-03-24","cited_by_count":11,"area":null},{"id":"2306.08076","title":"Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization","created":"2023-06-13","cited_by_count":10,"area":null},{"id":"2405.17003","title":"Graph Condensation for Open-World Graph Learning","created":"2024-05-27","cited_by_count":9,"area":null},{"id":"2408.03195","title":"RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning","created":"2024-08-06","cited_by_count":9,"area":"deeprl"},{"id":"2302.02909","title":"Spectral Augmentations for Graph Contrastive Learning","created":"2023-02-06","cited_by_count":8,"area":null},{"id":"2311.10579","title":"Graph Neural Networks for Pressure Estimation in Water Distribution Systems","created":"2023-11-17","cited_by_count":8,"area":null},{"id":"2401.03350","title":"Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks","created":"2024-01-07","cited_by_count":8,"area":null},{"id":"2208.02810","title":"Analyzing Data-Centric Properties for Graph Contrastive Learning","created":"2022-08-04","cited_by_count":7,"area":null},{"id":"2301.13060","title":"Zero-One Laws of Graph Neural Networks","created":"2023-01-30","cited_by_count":7,"area":null},{"id":"2402.11153","title":"Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs","created":"2024-02-17","cited_by_count":7,"area":null},{"id":"2404.13344","title":"GRANOLA: Adaptive Normalization for Graph Neural Networks","created":"2024-04-20","cited_by_count":7,"area":null},{"id":"2406.04601","title":"Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning","created":"2024-06-07","cited_by_count":7,"area":null},{"id":"2505.17293","title":"Graph Data Selection for Domain Adaptation: A Model-Free Approach","created":"2025-05-22","cited_by_count":7,"area":null},{"id":"2301.05860","title":"State of the Art and Potentialities of Graph-level Learning","created":"2023-01-14","cited_by_count":6,"area":null},{"id":"2312.10589","title":"NN-Steiner: A Mixed Neural-algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem","created":"2023-12-17","cited_by_count":6,"area":null},{"id":"2402.02287","title":"Future Directions in the Theory of Graph Machine Learning","created":"2024-02-03","cited_by_count":6,"area":null},{"id":"2409.05191","title":"Generalization of Geometric Graph Neural Networks with Lipschitz Loss Functions","created":"2024-09-08","cited_by_count":6,"area":null},{"id":"2510.06860","title":"Towards Generalization of Graph Neural Networks for AC Optimal Power Flow","created":"2025-10-08","cited_by_count":6,"area":null},{"id":"2204.09052","title":"Optimizing Tensor Network Contraction Using Reinforcement Learning","created":"2022-04-18","cited_by_count":5,"area":null},{"id":"2205.09575","title":"Learning Graph Structure from Convolutional Mixtures","created":"2022-05-19","cited_by_count":5,"area":null},{"id":"2404.14642","title":"Uncertainty Quantification on Graph Learning: A Survey","created":"2024-04-23","cited_by_count":4,"area":null},{"id":"2408.13878","title":"Generalization of Graph Neural Networks is Robust to Model Mismatch","created":"2024-08-25","cited_by_count":4,"area":null},{"id":"2507.07405","title":"HGMP:Heterogeneous Graph Multi-Task Prompt Learning","created":"2025-07-10","cited_by_count":4,"area":null},{"id":"2302.14806","title":"Framelet Message Passing","created":"2023-02-28","cited_by_count":3,"area":null},{"id":"2305.15611","title":"Tackling Size Generalization of Graph Neural Networks on Biological Data from a Spectral Perspective","created":"2023-05-24","cited_by_count":3,"area":null},{"id":"2310.05105","title":"How Graph Neural Networks Learn: Lessons from Training Dynamics","created":"2023-10-08","cited_by_count":3,"area":null},{"id":"2411.01035","title":"Provable Length Generalization in Sequence Prediction via Spectral Filtering","created":"2024-11-01","cited_by_count":3,"area":null},{"id":"2512.08763","title":"Learning and Editing Universal Graph Prompt Tuning via Reinforcement Learning","created":"2025-12-09","cited_by_count":3,"area":"deeprl"},{"id":"2310.06970","title":"Flood and Echo Net: Algorithmically Aligned GNNs that Generalize","created":"2023-10-10","cited_by_count":2,"area":null},{"id":"2310.10953","title":"A Local Graph Limits Perspective on Sampling-Based GNNs","created":"2023-10-17","cited_by_count":2,"area":null},{"id":"2404.05879","title":"Rapid and Precise Topological Comparison with Merge Tree Neural Networks","created":"2024-04-08","cited_by_count":2,"area":null},{"id":"2503.00547","title":"Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing","created":"2025-03-01","cited_by_count":2,"area":null},{"id":"2505.18028","title":"Knot So Simple: A Minimalistic Environment for Spatial Reasoning","created":"2025-05-23","cited_by_count":2,"area":"lr"},{"id":"2507.00927","title":"Understanding Generalization in Node and Link Prediction","created":"2025-07-01","cited_by_count":2,"area":null},{"id":"2607.17272","title":"Node4All: Learning Node Representation Beyond Datasets","created":"2026-07-19","cited_by_count":2,"area":null},{"id":"2111.03317","title":"Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters","created":"2021-11-05","cited_by_count":1,"area":null},{"id":"2202.03229","title":"Neural Models for Output-Space Invariance in Combinatorial Problems","created":"2022-02-07","cited_by_count":1,"area":null},{"id":"2207.14643","title":"Open World Learning Graph Convolution for Latency Estimation in Routing Networks","created":"2022-07-08","cited_by_count":1,"area":null},{"id":"2310.00873","title":"Deep Neural Networks Tend To Extrapolate Predictably","created":"2023-10-02","cited_by_count":1,"area":null},{"id":"2408.14381","title":"Learning Tree-Structured Composition of Data Augmentation","created":"2024-08-26","cited_by_count":1,"area":null},{"id":"2412.18734","title":"Predicting Time Series of Networked Dynamical Systems without Knowing Topology","created":"2024-12-25","cited_by_count":1,"area":null},{"id":"2506.06787","title":"FuncGNN: Learning Functional Semantics of Logic Circuits with Graph Neural Networks","created":"2025-06-07","cited_by_count":1,"area":null},{"id":"2508.18564","title":"A Note on Graphon-Signal Analysis of Graph Neural Networks","created":"2025-08-25","cited_by_count":1,"area":null},{"id":"2512.06154","title":"Learning Invariant Graph Representations Through Redundant Information","created":"2025-12-05","cited_by_count":1,"area":null},{"id":"2205.15555","title":"Graph-level Neural Networks: Current Progress and Future Directions","created":"2022-05-31","cited_by_count":0,"area":null},{"id":"2206.09002","title":"Cluster Generation via Deep Energy-Based Model","created":"2022-06-17","cited_by_count":0,"area":null},{"id":"2304.10328","title":"Learning Cellular Coverage from Real Network Configurations using GNNs","created":"2023-04-20","cited_by_count":0,"area":null},{"id":"2309.10976","title":"Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks","created":"2023-09-20","cited_by_count":0,"area":null},{"id":"2310.10909","title":"Heterogenous Memory Augmented Neural Networks","created":"2023-10-17","cited_by_count":0,"area":null},{"id":"2407.01886","title":"Core Knowledge Learning Framework for Graph Adaptation and Scalability Learning","created":"2024-07-02","cited_by_count":0,"area":null}],"next_cursor":"100"}