{"total":118,"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":"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":"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":"2305.08018","title":"DRew: Dynamically Rewired Message Passing with Delay","created":"2023-05-13","cited_by_count":82,"area":null},{"id":"2206.07369","title":"DiffWire: Inductive Graph Rewiring via the Lov\\'asz Bound","created":"2022-06-15","cited_by_count":79,"area":null},{"id":"2204.01855","title":"A Survey on Graph Representation Learning Methods","created":"2022-04-04","cited_by_count":66,"area":null},{"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":"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":"2208.03471","title":"Oversquashing in GNNs through the lens of information contraction and graph expansion","created":"2022-08-06","cited_by_count":53,"area":null},{"id":"2204.07321","title":"Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities","created":"2022-04-15","cited_by_count":52,"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":"2401.08514","title":"Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness","created":"2024-01-16","cited_by_count":39,"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":"2304.07702","title":"An Empirical Study of Realized GNN Expressiveness","created":"2023-04-16","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":"2204.10037","title":"DropMessage: Unifying Random Dropping for Graph Neural Networks","created":"2022-04-21","cited_by_count":29,"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":"2310.02156","title":"Probabilistically Rewired Message-Passing Neural Networks","created":"2023-10-03","cited_by_count":26,"area":null},{"id":"2304.01391","title":"Counterfactual Learning on Graphs: A Survey","created":"2023-04-03","cited_by_count":22,"area":null},{"id":"2205.10014","title":"A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection","created":"2022-05-20","cited_by_count":20,"area":null},{"id":"2205.15733","title":"Template based Graph Neural Network with Optimal Transport Distances","created":"2022-05-31","cited_by_count":20,"area":null},{"id":"2309.00976","title":"Pure Message Passing Can Estimate Common Neighbor for Link Prediction","created":"2023-09-02","cited_by_count":18,"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":"2202.10156","title":"1-WL Expressiveness Is (Almost) All You Need","created":"2022-02-21","cited_by_count":15,"area":null},{"id":"2405.17311","title":"Probabilistic Graph Rewiring via Virtual Nodes","created":"2024-05-27","cited_by_count":15,"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":"2507.21407","title":"Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects","created":"2025-07-29","cited_by_count":14,"area":"agents"},{"id":"2206.11010","title":"Agent-based Graph Neural Networks","created":"2022-06-22","cited_by_count":13,"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":"2407.01214","title":"Revisiting Random Walks for Learning on Graphs","created":"2024-07-01","cited_by_count":13,"area":null},{"id":"2502.04891","title":"GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring","created":"2025-02-07","cited_by_count":13,"area":null},{"id":"2306.04107","title":"BeMap: Balanced Message Passing for Fair Graph Neural Network","created":"2023-06-07","cited_by_count":11,"area":null},{"id":"2308.06838","title":"Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes","created":"2023-08-13","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":"2408.11370","title":"Graph Classification via Reference Distribution Learning: Theory and Practice","created":"2024-08-21","cited_by_count":11,"area":null},{"id":"2202.07114","title":"Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack","created":"2022-02-15","cited_by_count":10,"area":null},{"id":"2310.07235","title":"Are GATs Out of Balance?","created":"2023-10-11","cited_by_count":10,"area":null},{"id":"2203.09020","title":"Graph Augmentation Learning","created":"2022-03-17","cited_by_count":9,"area":null},{"id":"2206.05070","title":"Fundamental Limits in Formal Verification of Message-Passing Neural Networks","created":"2022-06-10","cited_by_count":9,"area":null},{"id":"2302.10574","title":"MulGT: Multi-task Graph-Transformer with Task-aware Knowledge Injection and Domain Knowledge-driven Pooling for Whole Slide Image Analysis","created":"2023-02-21","cited_by_count":9,"area":null},{"id":"2308.08344","title":"Graph Out-of-Distribution Generalization with Controllable Data Augmentation","created":"2023-08-16","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":"2201.05158","title":"Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach","created":"2022-01-13","cited_by_count":8,"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":"2310.06417","title":"Supercharging Graph Transformers with Advective Diffusion","created":"2023-10-10","cited_by_count":7,"area":"diffusion"},{"id":"2405.08424","title":"Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More","created":"2024-05-14","cited_by_count":7,"area":null},{"id":"2410.01669","title":"Sparse Covariance Neural Networks","created":"2024-10-02","cited_by_count":7,"area":null},{"id":"2305.15747","title":"Union Subgraph Neural Networks","created":"2023-05-25","cited_by_count":6,"area":null},{"id":"2310.09516","title":"Efficient Link Prediction via GNN Layers Induced by Negative Sampling","created":"2023-10-14","cited_by_count":6,"area":null},{"id":"2403.01475","title":"Representation Learning on Heterophilic Graph with Directional Neighborhood Attention","created":"2024-03-03","cited_by_count":6,"area":null},{"id":"2406.00418","title":"GATE: How to Keep Out Intrusive Neighbors","created":"2024-06-01","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":"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":"2304.10851","title":"What Do GNNs Actually Learn? Towards Understanding their Representations","created":"2023-04-21","cited_by_count":4,"area":null},{"id":"2305.15745","title":"Robust Ante-hoc Graph Explainer using Bilevel Optimization","created":"2023-05-25","cited_by_count":4,"area":null},{"id":"2306.12726","title":"On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling","created":"2023-06-22","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":"2406.12841","title":"Demystifying Higher-Order Graph Neural Networks","created":"2024-06-18","cited_by_count":4,"area":null},{"id":"2502.13660","title":"Towards Invariance to Node Identifiers in Graph Neural Networks","created":"2025-02-19","cited_by_count":4,"area":null},{"id":"2206.00637","title":"Graph Neural Networks with Precomputed Node Features","created":"2022-06-01","cited_by_count":3,"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":"2209.00936","title":"A Class-Aware Representation Refinement Framework for Graph Classification","created":"2022-09-02","cited_by_count":3,"area":null},{"id":"2210.10643","title":"Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning","created":"2022-10-19","cited_by_count":3,"area":null},{"id":"2310.07136","title":"Exponential Quantum Communication Advantage in Distributed Inference and Learning","created":"2023-10-11","cited_by_count":3,"area":null},{"id":"2405.16041","title":"Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models","created":"2024-05-25","cited_by_count":3,"area":null},{"id":"2407.03125","title":"Foundations and Frontiers of Graph Learning Theory","created":"2024-07-03","cited_by_count":3,"area":null},{"id":"2407.20067","title":"xAI-Drop: Don't Use What You Cannot Explain","created":"2024-07-29","cited_by_count":3,"area":null},{"id":"2409.09888","title":"Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning","created":"2024-09-15","cited_by_count":3,"area":null},{"id":"2111.11840","title":"Subgraph Permutation Equivariant Networks","created":"2021-11-23","cited_by_count":2,"area":null},{"id":"2204.10390","title":"SoftEdge: Regularizing Graph Classification with Random Soft Edges","created":"2022-04-21","cited_by_count":2,"area":null},{"id":"2204.13429","title":"DOTIN: Dropping Task-Irrelevant Nodes for GNNs","created":"2022-04-28","cited_by_count":2,"area":null},{"id":"2206.06758","title":"Universally Expressive Communication in Multi-Agent Reinforcement Learning","created":"2022-06-14","cited_by_count":2,"area":null},{"id":"2210.02060","title":"Graph Classification via Discriminative Edge Feature Learning","created":"2022-10-05","cited_by_count":2,"area":null},{"id":"2210.03123","title":"On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing","created":"2022-10-06","cited_by_count":2,"area":null},{"id":"2310.06970","title":"Flood and Echo Net: Algorithmically Aligned GNNs that Generalize","created":"2023-10-10","cited_by_count":2,"area":null},{"id":"2402.11339","title":"Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking","created":"2024-02-17","cited_by_count":2,"area":null},{"id":"2403.09961","title":"Thermal Earth Model for the Conterminous United States Using an Interpolative Physics-Informed Graph Neural Network (InterPIGNN)","created":"2024-03-15","cited_by_count":2,"area":null},{"id":"2404.04969","title":"Temporal Generalization Estimation in Evolving Graphs","created":"2024-04-07","cited_by_count":2,"area":null},{"id":"2405.03481","title":"AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers","created":"2024-05-06","cited_by_count":2,"area":null},{"id":"2405.10633","title":"Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks","created":"2024-05-17","cited_by_count":2,"area":null},{"id":"2406.11714","title":"Scalable Expressiveness through Preprocessed Graph Perturbations","created":"2024-06-17","cited_by_count":2,"area":null},{"id":"2406.15797","title":"$\\texttt{SynC}$: Synergistic Boosting of Structure and Representation for Deep Graph Clustering","created":"2024-06-22","cited_by_count":2,"area":null},{"id":"2410.01308","title":"How Hard Is It for Message-Passing GNNs to Simulate One Weisfeiler-Lehman Color-Refinement Step?","created":"2024-10-02","cited_by_count":2,"area":null},{"id":"2411.02271","title":"On the Utilization of Unique Node Identifiers in Graph Neural Networks","created":"2024-11-04","cited_by_count":2,"area":null},{"id":"2502.02479","title":"Using Random Noise Equivariantly to Boost Graph Neural Networks Universally","created":"2025-02-04","cited_by_count":2,"area":null},{"id":"2505.11416","title":"MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection","created":"2025-05-16","cited_by_count":2,"area":null},{"id":"2506.01728","title":"Principled Data Augmentation for Learning to Solve Quadratic Programming Problems","created":"2025-06-02","cited_by_count":2,"area":null},{"id":"2303.08958","title":"NESS: Node Embeddings from Static SubGraphs","created":"2023-03-15","cited_by_count":1,"area":null},{"id":"2405.20012","title":"FlexiDrop: Theoretical Insights and Practical Advances in Random Dropout Method on GNNs","created":"2024-05-30","cited_by_count":1,"area":null},{"id":"2412.08835","title":"Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNs","created":"2024-12-12","cited_by_count":1,"area":null}],"next_cursor":"100"}