{"total":52,"rows":[{"id":"2302.02941","title":"On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology","created":"2023-02-06","cited_by_count":166,"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":"2204.01855","title":"A Survey on Graph Representation Learning Methods","created":"2022-04-04","cited_by_count":66,"area":null},{"id":"2305.12788","title":"GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs","created":"2023-05-22","cited_by_count":58,"area":null},{"id":"2310.01668","title":"Locality-Aware Graph-Rewiring in GNNs","created":"2023-10-02","cited_by_count":56,"area":null},{"id":"2201.12674","title":"Rewiring with Positional Encodings for Graph Neural Networks","created":"2022-01-29","cited_by_count":36,"area":null},{"id":"2305.12398","title":"Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition","created":"2023-05-21","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":"2306.02376","title":"Towards Deep Attention in Graph Neural Networks: Problems and Remedies","created":"2023-06-04","cited_by_count":24,"area":null},{"id":"2308.15568","title":"Over-Squashing in Graph Neural Networks: A Comprehensive survey","created":"2023-08-29","cited_by_count":23,"area":null},{"id":"2510.16990","title":"Graph4MM: Weaving Multimodal Learning with Structural Information","created":"2025-10-19","cited_by_count":17,"area":null},{"id":"2109.02474","title":"TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting","created":"2021-08-25","cited_by_count":16,"area":null},{"id":"2109.09506","title":"Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference","created":"2021-09-16","cited_by_count":15,"area":null},{"id":"2310.02275","title":"MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data","created":"2023-09-29","cited_by_count":14,"area":null},{"id":"2405.13526","title":"Understanding Virtual Nodes: Oversquashing and Node Heterogeneity","created":"2024-05-22","cited_by_count":14,"area":null},{"id":"2210.11794","title":"Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences","created":"2022-10-21","cited_by_count":12,"area":null},{"id":"2312.04234","title":"Graph Convolutions Enrich the Self-Attention in Transformers!","created":"2023-12-07","cited_by_count":11,"area":null},{"id":"2110.02667","title":"Attentive Walk-Aggregating Graph Neural Networks","created":"2021-10-06","cited_by_count":10,"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":"2211.11853","title":"Learnable Graph Convolutional Attention Networks","created":"2022-11-21","cited_by_count":8,"area":null},{"id":"2410.05565","title":"Chain and Causal Attention for Efficient Entity Tracking","created":"2024-10-07","cited_by_count":8,"area":null},{"id":"2208.00374","title":"Neuro-Symbolic Learning: Principles and Applications in Ophthalmology","created":"2022-07-31","cited_by_count":7,"area":null},{"id":"2210.07715","title":"Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning","created":"2022-10-14","cited_by_count":6,"area":null},{"id":"2406.06581","title":"Order-Independence Without Fine Tuning","created":"2024-06-04","cited_by_count":6,"area":null},{"id":"2502.21185","title":"A Survey of Link Prediction in Temporal Networks","created":"2025-02-28","cited_by_count":6,"area":null},{"id":"2310.05296","title":"Tailoring Self-Attention for Graph via Rooted Subtrees","created":"2023-10-08","cited_by_count":5,"area":null},{"id":"2209.13539","title":"Spiking GATs: Learning Graph Attentions via Spiking Neural Network","created":"2022-09-05","cited_by_count":3,"area":null},{"id":"2305.05020","title":"Domain independent post-processing with graph U-nets: Applications to Electrical Impedance Tomographic Imaging","created":"2023-05-08","cited_by_count":3,"area":null},{"id":"2305.15822","title":"Towards Label Position Bias in Graph Neural Networks","created":"2023-05-25","cited_by_count":3,"area":null},{"id":"2412.11450","title":"GroupFace: Imbalanced Age Estimation Based on Multi-hop Attention Graph Convolutional Network and Group-aware Margin Optimization","created":"2024-12-16","cited_by_count":3,"area":null},{"id":"2204.03827","title":"IA-GCN: Interactive Graph Convolutional Network for Recommendation","created":"2022-04-08","cited_by_count":2,"area":null},{"id":"2212.05581","title":"Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition","created":"2022-12-11","cited_by_count":2,"area":null},{"id":"2312.05611","title":"Triplet Edge Attention for Algorithmic Reasoning","created":"2023-12-09","cited_by_count":2,"area":null},{"id":"2511.17923","title":"Towards Efficient LLM-aware Heterogeneous Graph Learning","created":"2025-11-22","cited_by_count":2,"area":null},{"id":"2110.07436","title":"Asymmetric Graph Representation Learning","created":"2021-10-14","cited_by_count":1,"area":null},{"id":"2206.07253","title":"TeKo: Text-Rich Graph Neural Networks with External Knowledge","created":"2022-06-15","cited_by_count":1,"area":null},{"id":"2206.08582","title":"DFG-NAS: Deep and Flexible Graph Neural Architecture Search","created":"2022-06-17","cited_by_count":1,"area":null},{"id":"2512.18228","title":"Toward Efficient Testing of Graph Neural Networks via Test Input Prioritization","created":"2025-12-20","cited_by_count":1,"area":null},{"id":"2605.08679","title":"Attention-based graph neural networks: a survey","created":"2026-05-09","cited_by_count":1,"area":null},{"id":"2307.15377","title":"Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning","created":"2023-07-28","cited_by_count":0,"area":null},{"id":"2401.01233","title":"GEN: A Practical Alternative to Graph Transformers for Long-Range Graph Modeling","created":"2024-01-02","cited_by_count":0,"area":null},{"id":"2402.06908","title":"Topological Neural Networks: Mitigating the Bottlenecks of Graph Neural Networks via Higher-Order Interactions","created":"2024-02-10","cited_by_count":0,"area":null},{"id":"2406.09954","title":"Rule Based Learning with Dynamic (Graph) Neural Networks","created":"2024-06-14","cited_by_count":0,"area":null},{"id":"2411.12052","title":"HoGA: Higher-Order Graph Attention via Diversity-Aware k-Hop Sampling","created":"2024-11-18","cited_by_count":0,"area":null},{"id":"2501.00773","title":"OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Tasks","created":"2025-01-01","cited_by_count":0,"area":null},{"id":"2504.17834","title":"Unveiling the Hidden: Movie Genre and User Bias in Spoiler Detection","created":"2025-04-24","cited_by_count":0,"area":null},{"id":"2509.21699","title":"Exact Subgraph Isomorphism Network with Mixed $L_{0,2}$ Norm Constraint for Predictive Graph Mining","created":"2025-09-25","cited_by_count":0,"area":null},{"id":"2510.20236","title":"Layer-to-Layer Knowledge Mixing in Graph Neural Network for Chemical Property Prediction","created":"2025-10-23","cited_by_count":0,"area":null},{"id":"2511.13062","title":"Self-Adaptive Graph Mixture of Models","created":"2025-11-17","cited_by_count":0,"area":null},{"id":"2606.00757","title":"RADE: Random Add-Drop Edge as a Regularizer","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2606.19185","title":"AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2609.02216","title":"PEARL: Path-Entity Aligned Relational Learning with Contextual Subgraphs for Inductive Knowledge Graph Completion","created":"2026-09-02","cited_by_count":0,"area":null}],"next_cursor":null}