{"list":"other","total":61,"rows":[{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1801.07606","title":"Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning","created":"2018-01-22","cited_by_count":1102},{"id":"1811.05868","title":"Pitfalls of Graph Neural Network Evaluation","created":"2018-11-14","cited_by_count":1007},{"id":"1806.03536","title":"Representation Learning on Graphs with Jumping Knowledge Networks","created":"2018-06-09","cited_by_count":877},{"id":"2007.02133","title":"Simple and Deep Graph Convolutional Networks","created":"2020-07-04","cited_by_count":860},{"id":"1907.10903","title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification","created":"2019-07-25","cited_by_count":680},{"id":"2003.00982","title":"Benchmarking Graph Neural Networks","created":"2020-03-02","cited_by_count":680},{"id":"2006.05205","title":"On the Bottleneck of Graph Neural Networks and its Practical Implications","created":"2020-06-09","cited_by_count":631},{"id":"1909.03211","title":"Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View","created":"2019-09-07","cited_by_count":545},{"id":"2111.14522","title":"Understanding over-squashing and bottlenecks on graphs via curvature","created":"2021-11-29","cited_by_count":478},{"id":"1910.12933","title":"Hyperbolic Graph Convolutional Neural Networks","created":"2019-10-28","cited_by_count":402},{"id":"1711.07553","title":"Residual Gated Graph ConvNets","created":"2017-11-20","cited_by_count":346},{"id":"2302.11640","title":"A critical look at the evaluation of GNNs under heterophily: Are we really making progress?","created":"2023-02-22","cited_by_count":343},{"id":"1909.12223","title":"PairNorm: Tackling Oversmoothing in GNNs","created":"2019-09-26","cited_by_count":297},{"id":"2007.09296","title":"Towards Deeper Graph Neural Networks","created":"2020-07-18","cited_by_count":296},{"id":"2302.03660","title":"Flow Matching on General Geometries","created":"2023-02-07","cited_by_count":265},{"id":"1910.12892","title":"Hyperbolic Graph Neural Networks","created":"2019-10-28","cited_by_count":244},{"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},{"id":"2402.00789","title":"Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces","created":"2024-02-01","cited_by_count":134},{"id":"1911.05076","title":"Constant Curvature Graph Convolutional Networks","created":"2019-11-12","cited_by_count":108},{"id":"2205.12156","title":"Not too little, not too much: a theoretical analysis of graph (over)smoothing","created":"2022-05-24","cited_by_count":106},{"id":"2210.11790","title":"FoSR: First-order spectral rewiring for addressing oversquashing in GNNs","created":"2022-10-21","cited_by_count":105},{"id":"2211.15779","title":"Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature","created":"2022-11-28","cited_by_count":98},{"id":"2302.06835","title":"Understanding Oversquashing in GNNs through the Lens of Effective Resistance","created":"2023-02-14","cited_by_count":97},{"id":"2305.08018","title":"DRew: Dynamically Rewired Message Passing with Delay","created":"2023-05-13","cited_by_count":82},{"id":"2206.07369","title":"DiffWire: Inductive Graph Rewiring via the Lov\\'asz Bound","created":"2022-06-15","cited_by_count":79},{"id":"2210.02997","title":"Expander Graph Propagation","created":"2022-10-06","cited_by_count":75},{"id":"2211.14208","title":"GREAD: Graph Neural Reaction-Diffusion Networks","created":"2022-11-25","cited_by_count":54},{"id":"2006.07107","title":"Understanding and Resolving Performance Degradation in Graph Convolutional Networks","created":"2020-06-12","cited_by_count":53},{"id":"2208.03471","title":"Oversquashing in GNNs through the lens of information contraction and graph expansion","created":"2022-08-06","cited_by_count":53},{"id":"2104.02228","title":"Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs","created":"2021-04-06","cited_by_count":50},{"id":"2210.00513","title":"Gradient Gating for Deep Multi-Rate Learning on Graphs","created":"2022-10-02","cited_by_count":49},{"id":"2502.10818","title":"On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning","created":"2025-02-15","cited_by_count":43},{"id":"2502.02017","title":"Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment","created":"2025-02-04","cited_by_count":42},{"id":"2309.09384","title":"Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature","created":"2023-09-17","cited_by_count":41},{"id":"2303.06562","title":"ContraNorm: A Contrastive Learning Perspective on Oversmoothing and Beyond","created":"2023-03-12","cited_by_count":39},{"id":"2502.03251","title":"RiemannGFM: Learning a Graph Foundation Model from Riemannian Geometry","created":"2025-02-05","cited_by_count":35},{"id":"2403.16030","title":"VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections","created":"2024-03-24","cited_by_count":34},{"id":"2406.02997","title":"Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs","created":"2024-06-05","cited_by_count":34},{"id":"2112.05393","title":"A Self-supervised Mixed-curvature Graph Neural Network","created":"2021-12-10","cited_by_count":33},{"id":"2211.17068","title":"Self-Supervised Continual Graph Learning in Adaptive Riemannian Spaces","created":"2022-11-30","cited_by_count":32},{"id":"2106.03134","title":"Pseudo-Riemannian Graph Convolutional Networks","created":"2021-06-06","cited_by_count":30},{"id":"2405.01009","title":"On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems","created":"2024-05-02","cited_by_count":28},{"id":"2208.14073","title":"A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning","created":"2022-08-30","cited_by_count":24},{"id":"2404.04612","title":"Spectral Graph Pruning Against Over-Squashing and Over-Smoothing","created":"2024-04-06","cited_by_count":24},{"id":"2405.12474","title":"How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing","created":"2024-05-21","cited_by_count":24},{"id":"2401.01232","title":"Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning","created":"2024-01-02","cited_by_count":23},{"id":"2405.11801","title":"LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering","created":"2024-05-20","cited_by_count":21},{"id":"2405.11951","title":"Distinguished In Uniform: Self Attention Vs. Virtual Nodes","created":"2024-05-20","cited_by_count":20},{"id":"2401.12780","title":"DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing","created":"2024-01-23","cited_by_count":16},{"id":"2410.17941","title":"Spiking Graph Neural Network on Riemannian Manifolds","created":"2024-10-23","cited_by_count":16},{"id":"2305.03555","title":"Contrastive Graph Clustering in Curvature Spaces","created":"2023-05-05","cited_by_count":15},{"id":"2405.13526","title":"Understanding Virtual Nodes: Oversquashing and Node Heterogeneity","created":"2024-05-22","cited_by_count":14},{"id":"2411.16278","title":"Even Sparser Graph Transformers","created":"2024-11-25","cited_by_count":13},{"id":"2406.03671","title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","created":"2024-06-06","cited_by_count":10},{"id":"2410.05499","title":"Unitary convolutions for learning on graphs and groups","created":"2024-10-07","cited_by_count":8},{"id":"2412.19993","title":"Discrete Curvature Graph Information Bottleneck","created":"2024-12-28","cited_by_count":4},{"id":"2502.03236","title":"Pioneer: Physics-informed Riemannian Graph ODE for Entropy-increasing Dynamics","created":"2025-02-05","cited_by_count":4},{"id":"2412.03825","title":"Residual Hyperbolic Graph Convolution Networks","created":"2024-12-05","cited_by_count":2}],"next_cursor":null}