{"total":172,"rows":[{"id":"2205.12454","title":"Recipe for a General, Powerful, Scalable Graph Transformer","created":"2022-05-25","cited_by_count":713,"area":null},{"id":"2110.07875","title":"Graph Neural Networks with Learnable Structural and Positional Representations","created":"2021-10-15","cited_by_count":333,"area":null},{"id":"2102.09544","title":"Combinatorial optimization and reasoning with graph neural networks","created":"2021-02-18","cited_by_count":268,"area":null},{"id":"2106.12575","title":"Weisfeiler and Lehman Go Cellular: CW Networks","created":"2021-06-23","cited_by_count":218,"area":null},{"id":"2101.10320","title":"Identity-aware Graph Neural Networks","created":"2021-01-25","cited_by_count":198,"area":null},{"id":"2201.12872","title":"Discovering Invariant Rationales for Graph Neural Networks","created":"2022-01-30","cited_by_count":188,"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":"2205.11172","title":"How Powerful are Spectral Graph Neural Networks","created":"2022-05-23","cited_by_count":169,"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":"2307.01026","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs","created":"2023-07-03","cited_by_count":138,"area":null},{"id":"2110.13197","title":"Nested Graph Neural Networks","created":"2021-10-25","cited_by_count":137,"area":null},{"id":"2306.02896","title":"Representational Strengths and Limitations of Transformers","created":"2023-06-05","cited_by_count":133,"area":null},{"id":"2106.06610","title":"Scalars are universal: Equivariant machine learning, structured like classical physics","created":"2021-06-11","cited_by_count":127,"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":"2102.06790","title":"A Unified Lottery Ticket Hypothesis for Graph Neural Networks","created":"2021-02-12","cited_by_count":118,"area":null},{"id":"2111.06283","title":"DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks","created":"2021-11-11","cited_by_count":118,"area":null},{"id":"2203.00199","title":"Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks","created":"2022-03-01","cited_by_count":94,"area":null},{"id":"2212.13350","title":"A Generalization of ViT/MLP-Mixer to Graphs","created":"2022-12-27","cited_by_count":93,"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":"2301.09308","title":"On the Expressive Power of Geometric Graph Neural Networks","created":"2023-01-23","cited_by_count":90,"area":null},{"id":"2102.07835","title":"Topological Graph Neural Networks","created":"2021-02-15","cited_by_count":87,"area":null},{"id":"2302.04181","title":"Attending to Graph Transformers","created":"2023-02-08","cited_by_count":83,"area":null},{"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":"2403.01232","title":"Polynormer: Polynomial-Expressive Graph Transformer in Linear Time","created":"2024-03-02","cited_by_count":65,"area":null},{"id":"2204.04661","title":"Expressiveness and Approximation Properties of Graph Neural Networks","created":"2022-04-10","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":"2308.16406","title":"CktGNN: Circuit Graph Neural Network for Electronic Design Automation","created":"2023-08-31","cited_by_count":58,"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":"2105.04550","title":"Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth","created":"2021-05-10","cited_by_count":55,"area":null},{"id":"2403.04468","title":"A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges","created":"2024-03-07","cited_by_count":54,"area":null},{"id":"2106.06707","title":"Graph Neural Networks with Local Graph Parameters","created":"2021-06-12","cited_by_count":52,"area":null},{"id":"2201.07083","title":"A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants","created":"2022-01-18","cited_by_count":52,"area":null},{"id":"2205.14210","title":"MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers","created":"2022-05-27","cited_by_count":49,"area":null},{"id":"2406.18380","title":"KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning","created":"2024-06-26","cited_by_count":49,"area":null},{"id":"2202.11097","title":"Message passing all the way up","created":"2022-02-22","cited_by_count":47,"area":null},{"id":"2305.08048","title":"Towards Understanding the Generalization of Graph Neural Networks","created":"2023-05-14","cited_by_count":41,"area":null},{"id":"2209.12288","title":"On Representing Linear Programs by Graph Neural Networks","created":"2022-09-25","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":"2108.10521","title":"Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study","created":"2021-08-24","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":"2305.15987","title":"A graphon-signal analysis of graph neural networks","created":"2023-05-25","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":"2307.01646","title":"SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation","created":"2023-07-04","cited_by_count":28,"area":"diffusion"},{"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":"2105.13099","title":"On the Universality of Graph Neural Networks on Large Random Graphs","created":"2021-05-27","cited_by_count":26,"area":null},{"id":"2306.02866","title":"Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance","created":"2023-06-05","cited_by_count":26,"area":null},{"id":"2406.14929","title":"Efficient Graph Similarity Computation with Alignment Regularization","created":"2024-06-21","cited_by_count":26,"area":null},{"id":"2109.12872","title":"Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks","created":"2021-09-27","cited_by_count":25,"area":null},{"id":"2306.03698","title":"Fine-grained Expressivity of Graph Neural Networks","created":"2023-06-06","cited_by_count":25,"area":null},{"id":"2106.03693","title":"Learning by Transference: Training Graph Neural Networks on Growing Graphs","created":"2021-06-07","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":"2211.03232","title":"Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks","created":"2022-11-06","cited_by_count":21,"area":null},{"id":"2302.11603","title":"Some Might Say All You Need Is Sum","created":"2023-02-22","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":"2203.07546","title":"Permutation Invariant Representations with Applications to Graph Deep Learning","created":"2022-03-14","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":"2405.11951","title":"Distinguished In Uniform: Self Attention Vs. Virtual Nodes","created":"2024-05-20","cited_by_count":20,"area":null},{"id":"2207.11311","title":"Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective","created":"2022-07-22","cited_by_count":19,"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":"2204.00887","title":"Dimensionless machine learning: Imposing exact units equivariance","created":"2022-04-02","cited_by_count":17,"area":null},{"id":"2501.18739","title":"Beyond Message Passing: Neural Graph Pattern Machine","created":"2025-01-30","cited_by_count":17,"area":null},{"id":"2108.10733","title":"Graph Neural Networks: Methods, Applications, and Opportunities","created":"2021-08-24","cited_by_count":16,"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":"2306.03561","title":"CIN++: Enhancing Topological Message Passing","created":"2023-06-06","cited_by_count":14,"area":null},{"id":"2405.15540","title":"Bundle Neural Networks for message diffusion on graphs","created":"2024-05-24","cited_by_count":14,"area":null},{"id":"2105.07264","title":"Neural Trees for Learning on Graphs","created":"2021-05-15","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":"2209.14991","title":"Machine learning and invariant theory","created":"2022-09-29","cited_by_count":13,"area":null},{"id":"2305.10391","title":"Optimality of Message-Passing Architectures for Sparse Graphs","created":"2023-05-17","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":"2407.01214","title":"Revisiting Random Walks for Learning on Graphs","created":"2024-07-01","cited_by_count":13,"area":null},{"id":"2411.12732","title":"Benchmarking Positional Encodings for GNNs and Graph Transformers","created":"2024-11-19","cited_by_count":13,"area":null},{"id":"2110.03576","title":"Training Stable Graph Neural Networks Through Constrained Learning","created":"2021-10-07","cited_by_count":12,"area":null},{"id":"2205.14092","title":"Capturing Graphs with Hypo-Elliptic Diffusions","created":"2022-05-27","cited_by_count":12,"area":null},{"id":"2211.16494","title":"On the Ability of Graph Neural Networks to Model Interactions Between Vertices","created":"2022-11-29","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":"2402.07099","title":"Rethinking the Capacity of Graph Neural Networks for Branching Strategy","created":"2024-02-11","cited_by_count":12,"area":null},{"id":"2209.12054","title":"From Local to Global: Spectral-Inspired Graph Neural Networks","created":"2022-09-24","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":"2301.13821","title":"Complete Neural Networks for Complete Euclidean Graphs","created":"2023-01-31","cited_by_count":10,"area":null},{"id":"2402.02484","title":"Weisfeiler Leman for Euclidean Equivariant Machine Learning","created":"2024-02-04","cited_by_count":10,"area":null},{"id":"2101.08104","title":"A Generalized Weisfeiler-Lehman Graph Kernel","created":"2021-01-20","cited_by_count":9,"area":null},{"id":"2406.08210","title":"Expressivity and Generalization: Fragment-Biases for Molecular GNNs","created":"2024-06-12","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":"2108.02768","title":"Learning to Elect","created":"2021-08-05","cited_by_count":8,"area":null},{"id":"2201.12994","title":"MGNN: Graph Neural Networks Inspired by Distance Geometry Problem","created":"2022-01-31","cited_by_count":8,"area":null},{"id":"2406.03148","title":"Aligning Transformers with Weisfeiler-Leman","created":"2024-06-05","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":"2106.00089","title":"Node-Variant Graph Filters in Graph Neural Networks","created":"2021-05-31","cited_by_count":7,"area":null},{"id":"2211.02501","title":"Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations","created":"2022-11-04","cited_by_count":7,"area":null}],"next_cursor":"100"}