{"total":154,"rows":[{"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":"2305.17589","title":"Graph Inductive Biases in Transformers without Message Passing","created":"2023-05-27","cited_by_count":153,"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":"2301.09505","title":"Rethinking the Expressive Power of GNNs via Graph Biconnectivity","created":"2023-01-23","cited_by_count":119,"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":"2209.15240","title":"Universal Prompt Tuning for Graph Neural Networks","created":"2022-09-30","cited_by_count":100,"area":null},{"id":"2204.01613","title":"SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators","created":"2022-04-04","cited_by_count":99,"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":"2305.10498","title":"Edge Directionality Improves Learning on Heterophilic Graphs","created":"2023-05-17","cited_by_count":90,"area":null},{"id":"2402.08678","title":"Graph Mamba: Towards Learning on Graphs with State Space Models","created":"2024-02-13","cited_by_count":86,"area":"ssm"},{"id":"2302.07090","title":"A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests","created":"2023-02-14","cited_by_count":64,"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":"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":"2310.14753","title":"Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules","created":"2023-10-23","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":"2202.11097","title":"Message passing all the way up","created":"2022-02-22","cited_by_count":47,"area":null},{"id":"2210.05801","title":"Linkless Link Prediction via Relational Distillation","created":"2022-10-11","cited_by_count":39,"area":null},{"id":"2401.08514","title":"Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness","created":"2024-01-16","cited_by_count":39,"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":"2304.01575","title":"The expressive power of pooling in Graph Neural Networks","created":"2023-04-04","cited_by_count":35,"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":"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":"2303.02918","title":"Graph Positional Encoding via Random Feature Propagation","created":"2023-03-06","cited_by_count":31,"area":null},{"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":"2211.17113","title":"Weisfeiler and Leman Go Relational","created":"2022-11-30","cited_by_count":28,"area":"lr"},{"id":"2308.08235","title":"The Expressive Power of Graph Neural Networks: A Survey","created":"2023-08-16","cited_by_count":27,"area":null},{"id":"2306.02866","title":"Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance","created":"2023-06-05","cited_by_count":26,"area":null},{"id":"2402.03687","title":"Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation","created":"2024-02-06","cited_by_count":25,"area":"diffusion"},{"id":"2307.06046","title":"A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes","created":"2023-07-12","cited_by_count":24,"area":null},{"id":"2404.04612","title":"Spectral Graph Pruning Against Over-Squashing and Over-Smoothing","created":"2024-04-06","cited_by_count":24,"area":null},{"id":"2502.19512","title":"TRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphs","created":"2025-02-26","cited_by_count":24,"area":null},{"id":"2205.06002","title":"Learning Generalized Policies Without Supervision Using GNNs","created":"2022-05-12","cited_by_count":22,"area":null},{"id":"2302.05743","title":"Is Distance Matrix Enough for Geometric Deep Learning?","created":"2023-02-11","cited_by_count":22,"area":null},{"id":"2201.12176","title":"Generative Coarse-Graining of Molecular Conformations","created":"2022-01-28","cited_by_count":21,"area":null},{"id":"2302.01313","title":"Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types","created":"2023-02-02","cited_by_count":21,"area":null},{"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":"2210.09521","title":"A Practical, Progressively-Expressive GNN","created":"2022-10-18","cited_by_count":20,"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":"2308.10436","title":"Approximately Equivariant Graph Networks","created":"2023-08-21","cited_by_count":16,"area":null},{"id":"2308.11129","title":"Enhancing Graph Transformers with Hierarchical Distance Structural Encoding","created":"2023-08-22","cited_by_count":16,"area":null},{"id":"2402.08595","title":"Homomorphism Counts for Graph Neural Networks: All About That Basis","created":"2024-02-13","cited_by_count":16,"area":null},{"id":"2201.13410","title":"Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings","created":"2022-01-31","cited_by_count":15,"area":null},{"id":"2202.10156","title":"1-WL Expressiveness Is (Almost) All You Need","created":"2022-02-21","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":"2306.15963","title":"Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications","created":"2023-06-28","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":"2208.10428","title":"Equivariant Hypergraph Neural Networks","created":"2022-08-22","cited_by_count":14,"area":null},{"id":"2306.03561","title":"CIN++: Enhancing Topological Message Passing","created":"2023-06-06","cited_by_count":14,"area":null},{"id":"2310.19285","title":"Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes","created":"2023-10-30","cited_by_count":14,"area":null},{"id":"2404.07097","title":"Fast Encoder-Based 3D from Casual Videos via Point Track Processing","created":"2024-04-10","cited_by_count":14,"area":null},{"id":"2112.07436","title":"Graph Kernel Neural Networks","created":"2021-12-14","cited_by_count":13,"area":null},{"id":"2206.08561","title":"Boosting Graph Structure Learning with Dummy Nodes","created":"2022-06-17","cited_by_count":13,"area":null},{"id":"2206.11010","title":"Agent-based Graph Neural Networks","created":"2022-06-22","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":"2206.07729","title":"Taxonomy of Benchmarks in Graph Representation Learning","created":"2022-06-15","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":"2410.10051","title":"Towards Bridging Generalization and Expressivity of Graph Neural Networks","created":"2024-10-14","cited_by_count":12,"area":null},{"id":"2307.01180","title":"PlanE: Representation Learning over Planar Graphs","created":"2023-07-03","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":"2310.13829","title":"Universal Representation of Permutation-Invariant Functions on Vectors and Tensors","created":"2023-10-20","cited_by_count":10,"area":null},{"id":"2201.10129","title":"Convergence of Invariant Graph Networks","created":"2022-01-25","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":"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":"2510.01510","title":"Flock: A Knowledge Graph Foundation Model via Learning on Random Walks","created":"2025-10-01","cited_by_count":9,"area":null},{"id":"2402.08450","title":"Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products","created":"2024-02-13","cited_by_count":8,"area":null},{"id":"2406.09291","title":"A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening","created":"2024-06-13","cited_by_count":8,"area":null},{"id":"2410.09118","title":"FSW-GNN: A Bi-Lipschitz WL-Equivalent Graph Neural Network","created":"2024-10-10","cited_by_count":8,"area":null},{"id":"2403.08147","title":"Representing Molecules as Random Walks Over Interpretable Grammars","created":"2024-03-13","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":"2301.05860","title":"State of the Art and Potentialities of Graph-level Learning","created":"2023-01-14","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":"2406.06984","title":"On the H\\\"{o}lder Stability of Multiset and Graph Neural Networks","created":"2024-06-11","cited_by_count":6,"area":null},{"id":"2409.11504","title":"Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph","created":"2024-09-17","cited_by_count":6,"area":null},{"id":"2302.11349","title":"Steerable Equivariant Representation Learning","created":"2023-02-22","cited_by_count":5,"area":null},{"id":"2501.03113","title":"Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality","created":"2025-01-06","cited_by_count":5,"area":null},{"id":"2505.11298","title":"Graph Representational Learning: When Does More Expressivity Hurt Generalization?","created":"2025-05-16","cited_by_count":5,"area":null},{"id":"2205.12245","title":"Asynchronous Neural Networks for Learning in Graphs","created":"2022-05-24","cited_by_count":4,"area":null},{"id":"2206.02059","title":"Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm","created":"2022-06-04","cited_by_count":4,"area":null},{"id":"2304.10074","title":"Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick","created":"2023-04-20","cited_by_count":4,"area":null},{"id":"2304.10851","title":"What Do GNNs Actually Learn? Towards Understanding their Representations","created":"2023-04-21","cited_by_count":4,"area":null},{"id":"2311.16670","title":"PyTorch Geometric High Order: A Unified Library for High Order Graph Neural Network","created":"2023-11-28","cited_by_count":4,"area":null},{"id":"2312.05905","title":"Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings","created":"2023-12-10","cited_by_count":4,"area":null},{"id":"2405.02795","title":"Graph as Point Set","created":"2024-05-05","cited_by_count":4,"area":null},{"id":"2406.12841","title":"Demystifying Higher-Order Graph Neural Networks","created":"2024-06-18","cited_by_count":4,"area":null},{"id":"2412.07407","title":"Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings","created":"2024-12-10","cited_by_count":4,"area":null},{"id":"2503.00485","title":"Homomorphism Expressivity of Spectral Invariant Graph Neural Networks","created":"2025-03-01","cited_by_count":4,"area":null},{"id":"2503.19666","title":"Towards Efficient Training of Graph Neural Networks: A Multiscale Approach","created":"2025-03-25","cited_by_count":4,"area":null},{"id":"2506.02293","title":"On Universality Classes of Equivariant Networks","created":"2025-06-02","cited_by_count":4,"area":null},{"id":"2506.24018","title":"Bridging Theory and Practice in Link Representation with Graph Neural Networks","created":"2025-06-30","cited_by_count":4,"area":null},{"id":"2206.11876","title":"A Topological characterisation of Weisfeiler-Leman equivalence classes","created":"2022-06-23","cited_by_count":3,"area":null},{"id":"2302.10506","title":"Diffusion Probabilistic Models for Structured Node Classification","created":"2023-02-21","cited_by_count":3,"area":"diffusion"},{"id":"2304.07152","title":"Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability","created":"2023-04-14","cited_by_count":3,"area":null},{"id":"2305.05368","title":"Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptive Residual Module","created":"2023-05-09","cited_by_count":3,"area":null}],"next_cursor":"100"}