{"total":198,"rows":[{"id":"2106.06935","title":"Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction","created":"2021-06-13","cited_by_count":275,"area":null},{"id":"2310.00149","title":"One for All: Towards Training One Graph Model for All Classification Tasks","created":"2023-09-29","cited_by_count":243,"area":null},{"id":"2202.13013","title":"Sign and Basis Invariant Networks for Spectral Graph Representation Learning","created":"2022-02-25","cited_by_count":179,"area":null},{"id":"2110.03753","title":"From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness","created":"2021-10-07","cited_by_count":155,"area":null},{"id":"2110.02910","title":"Equivariant Subgraph Aggregation Networks","created":"2021-10-06","cited_by_count":154,"area":null},{"id":"2305.17589","title":"Graph Inductive Biases in Transformers without Message Passing","created":"2023-05-27","cited_by_count":153,"area":null},{"id":"2110.13197","title":"Nested Graph Neural Networks","created":"2021-10-25","cited_by_count":137,"area":null},{"id":"2202.08235","title":"Data Augmentation for Deep Graph Learning: A Survey","created":"2022-02-16","cited_by_count":133,"area":null},{"id":"2208.07239","title":"ROLAND: Graph Learning Framework for Dynamic Graphs","created":"2022-08-15","cited_by_count":132,"area":null},{"id":"2112.09992","title":"Weisfeiler and Leman go Machine Learning: The Story so far","created":"2021-12-18","cited_by_count":122,"area":null},{"id":"2206.11140","title":"Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries","created":"2022-06-22","cited_by_count":112,"area":null},{"id":"2407.20060","title":"RelBench: A Benchmark for Deep Learning on Relational Databases","created":"2024-07-29","cited_by_count":96,"area":null},{"id":"2205.13328","title":"How Powerful are K-hop Message Passing Graph Neural Networks","created":"2022-05-26","cited_by_count":90,"area":null},{"id":"2402.02216","title":"Position: Graph Foundation Models are Already Here","created":"2024-02-03","cited_by_count":86,"area":null},{"id":"2209.15486","title":"Graph Neural Networks for Link Prediction with Subgraph Sketching","created":"2022-09-30","cited_by_count":81,"area":null},{"id":"2405.11416","title":"Discrete-state Continuous-time Diffusion for Graph Generation","created":"2024-05-19","cited_by_count":76,"area":"diffusion"},{"id":"2204.01855","title":"A Survey on Graph Representation Learning Methods","created":"2022-04-04","cited_by_count":66,"area":null},{"id":"2110.00577","title":"Reconstruction for Powerful Graph Representations","created":"2021-10-01","cited_by_count":65,"area":null},{"id":"2110.13094","title":"Gophormer: Ego-Graph Transformer for Node Classification","created":"2021-10-25","cited_by_count":63,"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":"2205.10128","title":"Neural-Symbolic Models for Logical Queries on Knowledge Graphs","created":"2022-05-16","cited_by_count":61,"area":"lr"},{"id":"2206.11168","title":"Ordered Subgraph Aggregation Networks","created":"2022-06-22","cited_by_count":57,"area":null},{"id":"2210.13978","title":"Boosting the Cycle Counting Power of Graph Neural Networks with I$^2$-GNNs","created":"2022-10-22","cited_by_count":56,"area":null},{"id":"2209.01084","title":"Neighborhood-aware Scalable Temporal Network Representation Learning","created":"2022-09-02","cited_by_count":54,"area":null},{"id":"2107.01495","title":"On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs","created":"2021-07-03","cited_by_count":49,"area":null},{"id":"2201.12884","title":"A Theoretical Comparison of Graph Neural Network Extensions","created":"2022-01-30","cited_by_count":48,"area":null},{"id":"2502.06784","title":"RelGNN: Composite Message Passing for Relational Deep Learning","created":"2025-02-10","cited_by_count":48,"area":null},{"id":"2108.13049","title":"Single Node Injection Attack against Graph Neural Networks","created":"2021-08-30","cited_by_count":45,"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":"2302.11556","title":"Equivariant Polynomials for Graph Neural Networks","created":"2023-02-22","cited_by_count":38,"area":null},{"id":"2203.13913","title":"SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks","created":"2022-03-25","cited_by_count":37,"area":null},{"id":"2310.01267","title":"Cooperative Graph Neural Networks","created":"2023-10-02","cited_by_count":36,"area":null},{"id":"2505.10960","title":"Relational Graph Transformer","created":"2025-05-16","cited_by_count":34,"area":null},{"id":"2304.07702","title":"An Empirical Study of Realized GNN Expressiveness","created":"2023-04-16","cited_by_count":33,"area":null},{"id":"2310.02579","title":"On the Stability of Expressive Positional Encodings for Graphs","created":"2023-10-04","cited_by_count":33,"area":null},{"id":"2302.02209","title":"A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs","created":"2023-02-04","cited_by_count":32,"area":null},{"id":"2306.05955","title":"Path Neural Networks: Expressive and Accurate Graph Neural Networks","created":"2023-06-09","cited_by_count":32,"area":null},{"id":"2301.11039","title":"WL meet VC","created":"2023-01-26","cited_by_count":31,"area":null},{"id":"2206.07922","title":"Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications","created":"2022-06-16","cited_by_count":30,"area":null},{"id":"2210.12089","title":"A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges","created":"2022-10-21","cited_by_count":30,"area":null},{"id":"2306.11586","title":"Provably Powerful Graph Neural Networks for Directed Multigraphs","created":"2023-06-20","cited_by_count":27,"area":null},{"id":"2308.08235","title":"The Expressive Power of Graph Neural Networks: A Survey","created":"2023-08-16","cited_by_count":27,"area":null},{"id":"2502.01122","title":"Learning Efficient Positional Encodings with Graph Neural Networks","created":"2025-02-03","cited_by_count":24,"area":null},{"id":"2404.01039","title":"A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide","created":"2024-04-01","cited_by_count":23,"area":null},{"id":"2205.13954","title":"Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation","created":"2022-05-27","cited_by_count":22,"area":"distill"},{"id":"2310.18716","title":"Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding","created":"2023-10-28","cited_by_count":21,"area":null},{"id":"2203.10304","title":"PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs","created":"2022-03-19","cited_by_count":20,"area":null},{"id":"2210.09521","title":"A Practical, Progressively-Expressive GNN","created":"2022-10-18","cited_by_count":20,"area":null},{"id":"2602.12606","title":"RelBench v2: A Large-Scale Benchmark and Repository for Relational Data","created":"2026-02-13","cited_by_count":20,"area":null},{"id":"2210.02636","title":"Geodesic Graph Neural Network for Efficient Graph Representation Learning","created":"2022-10-06","cited_by_count":19,"area":null},{"id":"2304.08183","title":"Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion","created":"2023-04-17","cited_by_count":19,"area":null},{"id":"2305.13987","title":"On Structural Expressive Power of Graph Transformers","created":"2023-05-23","cited_by_count":19,"area":null},{"id":"2406.00134","title":"Anomaly Detection in Dynamic Graphs: A Comprehensive Survey","created":"2024-05-31","cited_by_count":19,"area":null},{"id":"2411.19513","title":"ContextGNN: Beyond Two-Tower Recommendation Systems","created":"2024-11-29","cited_by_count":19,"area":null},{"id":"2305.04963","title":"From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks","created":"2023-05-08","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":"2201.09398","title":"Towards Private Learning on Decentralized Graphs with Local Differential Privacy","created":"2022-01-23","cited_by_count":16,"area":null},{"id":"2109.02058","title":"Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model","created":"2021-09-05","cited_by_count":15,"area":null},{"id":"2205.14368","title":"Going Deeper into Permutation-Sensitive Graph Neural Networks","created":"2022-05-28","cited_by_count":15,"area":null},{"id":"2312.02339","title":"Expressive Sign Equivariant Networks for Spectral Geometric Learning","created":"2023-12-04","cited_by_count":15,"area":null},{"id":"2406.04336","title":"On the Expressive Power of Spectral Invariant Graph Neural Networks","created":"2024-06-06","cited_by_count":15,"area":null},{"id":"2107.04755","title":"Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering","created":"2021-07-10","cited_by_count":14,"area":null},{"id":"2203.10280","title":"Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily","created":"2022-03-19","cited_by_count":14,"area":null},{"id":"2407.11083","title":"Empowering Graph Invariance Learning with Deep Spurious Infomax","created":"2024-07-13","cited_by_count":14,"area":null},{"id":"2411.11829","title":"Tackling prediction tasks in relational databases with LLMs","created":"2024-11-18","cited_by_count":14,"area":null},{"id":"2303.10576","title":"An Efficient Subgraph GNN with Provable Substructure Counting Power","created":"2023-03-19","cited_by_count":13,"area":null},{"id":"2306.03266","title":"Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman","created":"2023-06-05","cited_by_count":13,"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":"2406.05815","title":"What Can We Learn from State Space Models for Machine Learning on Graphs?","created":"2024-06-09","cited_by_count":13,"area":null},{"id":"2206.04516","title":"Accurate Node Feature Estimation with Structured Variational Graph Autoencoder","created":"2022-06-09","cited_by_count":12,"area":null},{"id":"2210.03990","title":"Weisfeiler-Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs","created":"2022-10-08","cited_by_count":12,"area":null},{"id":"2309.04941","title":"Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power","created":"2023-09-10","cited_by_count":12,"area":null},{"id":"2209.07086","title":"Earthquake Phase Association with Graph Neural Networks","created":"2022-09-15","cited_by_count":11,"area":null},{"id":"2307.01180","title":"PlanE: Representation Learning over Planar Graphs","created":"2023-07-03","cited_by_count":11,"area":null},{"id":"2310.19142","title":"MAG-GNN: Reinforcement Learning Boosted Graph Neural Network","created":"2023-10-29","cited_by_count":11,"area":null},{"id":"2402.04836","title":"On the Completeness of Invariant Geometric Deep Learning Models","created":"2024-02-07","cited_by_count":11,"area":null},{"id":"2310.19263","title":"A Metadata-Driven Approach to Understand Graph Neural Networks","created":"2023-10-30","cited_by_count":10,"area":null},{"id":"2106.08992","title":"On the approximation capability of GNNs in node classification/regression tasks","created":"2021-06-16","cited_by_count":9,"area":null},{"id":"2203.09020","title":"Graph Augmentation Learning","created":"2022-03-17","cited_by_count":9,"area":null},{"id":"2206.12004","title":"Sampling Enclosing Subgraphs for Link Prediction","created":"2022-06-23","cited_by_count":9,"area":null},{"id":"2402.04621","title":"Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective","created":"2024-02-07","cited_by_count":9,"area":null},{"id":"2402.16346","title":"Boosting Graph Pooling with Persistent Homology","created":"2024-02-26","cited_by_count":9,"area":null},{"id":"2406.19244","title":"Improving the Expressiveness of $K$-hop Message-Passing GNNs by Injecting Contextualized Substructure Information","created":"2024-06-27","cited_by_count":9,"area":null},{"id":"2503.01079","title":"Depth-Adaptive Graph Neural Networks via Learnable Bakry-'Emery Curvature","created":"2025-03-03","cited_by_count":8,"area":null},{"id":"2506.01360","title":"RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databases","created":"2025-06-02","cited_by_count":8,"area":null},{"id":"2108.13650","title":"Heterogeneous Graph Neural Network with Multi-view Representation Learning","created":"2021-08-31","cited_by_count":7,"area":null},{"id":"2203.10866","title":"Unsupervised Network Embedding Beyond Homophily","created":"2022-03-21","cited_by_count":7,"area":null},{"id":"2212.14182","title":"WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning","created":"2022-12-29","cited_by_count":7,"area":null},{"id":"2303.12306","title":"Understanding Expressivity of GNN in Rule Learning","created":"2023-03-22","cited_by_count":7,"area":null},{"id":"2311.12741","title":"Content Augmented Graph Neural Networks","created":"2023-11-21","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":"2602.15022","title":"Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation","created":"2026-02-16","cited_by_count":7,"area":"diffusion"},{"id":"2202.08815","title":"GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs","created":"2022-02-17","cited_by_count":6,"area":null},{"id":"2206.09567","title":"Two-Dimensional Weisfeiler-Lehman Graph Neural Networks for Link Prediction","created":"2022-06-20","cited_by_count":6,"area":null},{"id":"2202.08833","title":"What Functions Can Graph Neural Networks Generate?","created":"2022-02-17","cited_by_count":5,"area":null},{"id":"2309.09068","title":"Recovering Missing Node Features with Local Structure-based Embeddings","created":"2023-09-16","cited_by_count":5,"area":null},{"id":"2309.12694","title":"Recurrent Temporal Revision Graph Networks","created":"2023-09-22","cited_by_count":5,"area":null},{"id":"2404.13604","title":"CKGConv: General Graph Convolution with Continuous Kernels","created":"2024-04-21","cited_by_count":5,"area":null},{"id":"2405.11911","title":"Accurate Link Prediction for Edge-Incomplete Graphs via PU Learning","created":"2024-05-20","cited_by_count":5,"area":null},{"id":"2406.02732","title":"GEFL: Extended Filtration Learning for Graph Classification","created":"2024-06-04","cited_by_count":5,"area":null}],"next_cursor":"100"}