{"list":"other","total":59,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"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":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"2312.00752","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","created":"2023-12-01","cited_by_count":4718},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"2005.00687","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","created":"2020-05-02","cited_by_count":2163},{"id":"1606.09375","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","created":"2016-06-30","cited_by_count":2089},{"id":"1902.07153","title":"Simplifying Graph Convolutional Networks","created":"2019-02-19","cited_by_count":1477},{"id":"2010.13902","title":"Graph Contrastive Learning with Augmentations","created":"2020-10-22","cited_by_count":1208},{"id":"1602.07576","title":"Group Equivariant Convolutional Networks","created":"2016-02-24","cited_by_count":1165},{"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":"2002.05287","title":"Geom-GCN: Geometric Graph Convolutional Networks","created":"2020-02-13","cited_by_count":826},{"id":"1806.08804","title":"Hierarchical Graph Representation Learning with Differentiable Pooling","created":"2018-06-22","cited_by_count":787},{"id":"2205.12454","title":"Recipe for a General, Powerful, Scalable Graph Transformer","created":"2022-05-25","cited_by_count":713},{"id":"2006.07988","title":"Adaptive Universal Generalized PageRank Graph Neural Network","created":"2020-06-14","cited_by_count":595},{"id":"1909.13021","title":"Multi-scale Attributed Node Embedding","created":"2019-09-28","cited_by_count":548},{"id":"1905.00067","title":"MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing","created":"2019-04-30","cited_by_count":443},{"id":"1812.09902","title":"Invariant and Equivariant Graph Networks","created":"2018-12-24","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":"1709.02540","title":"The Expressive Power of Neural Networks: A View from the Width","created":"2017-09-08","cited_by_count":320},{"id":"2110.14446","title":"Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods","created":"2021-10-27","cited_by_count":311},{"id":"2007.02901","title":"Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks","created":"2020-07-06","cited_by_count":260},{"id":"1411.5908","title":"Understanding image representations by measuring their equivariance and equivalence","created":"2014-11-21","cited_by_count":243},{"id":"2002.06157","title":"Generalization and Representational Limits of Graph Neural Networks","created":"2020-02-14","cited_by_count":218},{"id":"2106.05819","title":"Adversarial Graph Augmentation to Improve Graph Contrastive Learning","created":"2021-06-10","cited_by_count":200},{"id":"2202.02466","title":"Handling Distribution Shifts on Graphs: An Invariance Perspective","created":"2022-02-05","cited_by_count":164},{"id":"2106.05667","title":"GraphiT: Encoding Graph Structure in Transformers","created":"2021-06-10","cited_by_count":147},{"id":"2306.10759","title":"SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations","created":"2023-06-19","cited_by_count":133},{"id":"1710.11278","title":"Approximating Continuous Functions by ReLU Nets of Minimal Width","created":"2017-10-31","cited_by_count":122},{"id":"2102.11391","title":"MagNet: A Neural Network for Directed Graphs","created":"2021-02-22","cited_by_count":107},{"id":"2305.10498","title":"Edge Directionality Improves Learning on Heterophilic Graphs","created":"2023-05-17","cited_by_count":90},{"id":"2406.08993","title":"Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification","created":"2024-06-13","cited_by_count":85},{"id":"1905.01004","title":"Stability and Generalization of Graph Convolutional Neural Networks","created":"2019-05-03","cited_by_count":81},{"id":"2004.13970","title":"Directed Graph Convolutional Network","created":"2020-04-29","cited_by_count":73},{"id":"2403.01232","title":"Polynormer: Polynomial-Expressive Graph Transformer in Linear Time","created":"2024-03-02","cited_by_count":65},{"id":"1802.01572","title":"MotifNet: a motif-based Graph Convolutional Network for directed graphs","created":"2018-02-04","cited_by_count":64},{"id":"1906.02174","title":"Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks","created":"2019-06-05","cited_by_count":62},{"id":"2111.04840","title":"Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods","created":"2021-11-08","cited_by_count":58},{"id":"2402.03358","title":"A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation","created":"2024-01-29","cited_by_count":57},{"id":"2111.06748","title":"Simplifying approach to Node Classification in Graph Neural Networks","created":"2021-11-12","cited_by_count":56},{"id":"1610.04574","title":"Generalization Error of Invariant Classifiers","created":"2016-10-14","cited_by_count":42},{"id":"2310.19035","title":"Does Invariant Graph Learning via Environment Augmentation Learn Invariance?","created":"2023-10-29","cited_by_count":42},{"id":"2105.07634","title":"Improving Graph Neural Networks with Simple Architecture Design","created":"2021-05-17","cited_by_count":41},{"id":"1907.08990","title":"Spectral-based Graph Convolutional Network for Directed Graphs","created":"2019-07-21","cited_by_count":38},{"id":"2211.02843","title":"Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift","created":"2022-11-05","cited_by_count":38},{"id":"1908.05081","title":"AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models","created":"2019-08-14","cited_by_count":37},{"id":"2407.09618","title":"The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges","created":"2024-07-12","cited_by_count":29},{"id":"2210.13148","title":"Transformers over Directed Acyclic Graphs","created":"2022-10-24","cited_by_count":23},{"id":"2202.12449","title":"Directed Graph Auto-Encoders","created":"2022-02-25","cited_by_count":22},{"id":"2310.02232","title":"HoloNets: Spectral Convolutions do extend to Directed Graphs","created":"2023-10-03","cited_by_count":20},{"id":"2312.04111","title":"Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification","created":"2023-12-07","cited_by_count":19},{"id":"2107.10957","title":"Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks","created":"2021-07-22","cited_by_count":17},{"id":"1804.02081","title":"Adaptive Diffusions for Scalable Learning over Graphs","created":"2018-04-05","cited_by_count":12},{"id":"2403.03599","title":"Learning Invariant Representations of Graph Neural Networks via Cluster Generalization","created":"2024-03-06","cited_by_count":10},{"id":"2407.01635","title":"Commute Graph Neural Networks","created":"2024-06-30","cited_by_count":5},{"id":"2403.10572","title":"Discovering Invariant Neighborhood Patterns for Heterophilic Graphs","created":"2024-03-15","cited_by_count":4},{"id":"2006.11118","title":"Graph Pooling with Node Proximity for Hierarchical Representation Learning","created":"2020-06-19","cited_by_count":3}],"next_cursor":null}