{"list":"other","total":65,"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":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1701.06538","title":"Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer","created":"2017-01-23","cited_by_count":3509},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"2101.03961","title":"Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity","created":"2021-01-11","cited_by_count":3073},{"id":"1505.07818","title":"Domain-Adversarial Training of Neural Networks","created":"2015-05-28","cited_by_count":3044},{"id":"1907.02893","title":"Invariant Risk Minimization","created":"2019-07-05","cited_by_count":1854},{"id":"1611.03530","title":"Understanding deep learning requires rethinking generalization","created":"2016-11-10","cited_by_count":1674},{"id":"1911.08731","title":"Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization","created":"2019-11-20","cited_by_count":1247},{"id":"1607.01719","title":"Deep CORAL: Correlation Alignment for Deep Domain Adaptation","created":"2016-07-06","cited_by_count":1200},{"id":"1806.01973","title":"Graph Convolutional Neural Networks for Web-Scale Recommender Systems","created":"2018-06-06","cited_by_count":1138},{"id":"1906.07413","title":"Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss","created":"2019-06-18","cited_by_count":989},{"id":"1603.08861","title":"Revisiting Semi-Supervised Learning with Graph Embeddings","created":"2016-03-29","cited_by_count":976},{"id":"2112.06905","title":"GLaM: Efficient Scaling of Language Models with Mixture-of-Experts","created":"2021-12-13","cited_by_count":897},{"id":"2002.05287","title":"Geom-GCN: Geometric Graph Convolutional Networks","created":"2020-02-13","cited_by_count":826},{"id":"2106.05974","title":"Scaling Vision with Sparse Mixture of Experts","created":"2021-06-10","cited_by_count":747},{"id":"2003.00688","title":"Out-of-Distribution Generalization via Risk Extrapolation (REx)","created":"2020-03-02","cited_by_count":742},{"id":"2007.08663","title":"TUDataset: A collection of benchmark datasets for learning with graphs","created":"2020-07-16","cited_by_count":728},{"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":"2007.07314","title":"Long-tail learning via logit adjustment","created":"2020-07-14","cited_by_count":618},{"id":"2012.15445","title":"Explainability in Graph Neural Networks: A Taxonomic Survey","created":"2020-12-31","cited_by_count":384},{"id":"2002.01680","title":"MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding","created":"2020-02-05","cited_by_count":304},{"id":"2005.11079","title":"Graph Random Neural Network for Semi-Supervised Learning on Graphs","created":"2020-05-22","cited_by_count":213},{"id":"2006.06830","title":"Data Augmentation for Graph Neural Networks","created":"2020-06-11","cited_by_count":203},{"id":"2201.12872","title":"Discovering Invariant Rationales for Graph Neural Networks","created":"2022-01-30","cited_by_count":188},{"id":"2201.12987","title":"Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism","created":"2022-01-31","cited_by_count":171},{"id":"1905.02850","title":"Understanding Attention and Generalization in Graph Neural Networks","created":"2019-05-08","cited_by_count":169},{"id":"2202.02466","title":"Handling Distribution Shifts on Graphs: An Invariance Perspective","created":"2022-02-05","cited_by_count":164},{"id":"2209.01667","title":"A Review of Sparse Expert Models in Deep Learning","created":"2022-09-04","cited_by_count":148},{"id":"2202.07179","title":"G-Mixup: Graph Data Augmentation for Graph Classification","created":"2022-02-15","cited_by_count":146},{"id":"2202.05441","title":"Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs","created":"2022-02-11","cited_by_count":144},{"id":"2206.08452","title":"GOOD: A Graph Out-of-Distribution Benchmark","created":"2022-06-16","cited_by_count":141},{"id":"2202.08235","title":"Data Augmentation for Deep Graph Learning: A Survey","created":"2022-02-16","cited_by_count":133},{"id":"2010.08853","title":"From Local Structures to Size Generalization in Graph Neural Networks","created":"2020-10-17","cited_by_count":124},{"id":"2104.06643","title":"Generative Causal Explanations for Graph Neural Networks","created":"2021-04-14","cited_by_count":124},{"id":"2005.07959","title":"Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models","created":"2020-05-16","cited_by_count":116},{"id":"2112.15089","title":"Causal Attention for Interpretable and Generalizable Graph Classification","created":"2021-12-30","cited_by_count":108},{"id":"2202.07987","title":"Out-Of-Distribution Generalization on Graphs: A Survey","created":"2022-02-16","cited_by_count":97},{"id":"2108.01099","title":"Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data","created":"2021-08-02","cited_by_count":87},{"id":"2206.04046","title":"Sparse Mixture-of-Experts are Domain Generalizable Learners","created":"2022-06-08","cited_by_count":85},{"id":"2103.05045","title":"Size-Invariant Graph Representations for Graph Classification Extrapolations","created":"2021-03-08","cited_by_count":84},{"id":"2010.05421","title":"Factorizable Graph Convolutional Networks","created":"2020-10-12","cited_by_count":79},{"id":"2112.03806","title":"OOD-GNN: Out-of-Distribution Generalized Graph Neural Network","created":"2021-12-07","cited_by_count":78},{"id":"2111.10657","title":"Generalizing Graph Neural Networks on Out-Of-Distribution Graphs","created":"2021-11-20","cited_by_count":71},{"id":"2206.02886","title":"Graph Rationalization with Environment-based Augmentations","created":"2022-06-06","cited_by_count":70},{"id":"2106.15535","title":"Subgroup Generalization and Fairness of Graph Neural Networks","created":"2021-06-29","cited_by_count":69},{"id":"2210.03561","title":"Empowering Graph Representation Learning with Test-Time Graph Transformation","created":"2022-10-07","cited_by_count":67},{"id":"2010.09891","title":"Robust Optimization as Data Augmentation for Large-scale Graphs","created":"2020-10-19","cited_by_count":58},{"id":"2206.07869","title":"Let Invariant Rationale Discovery Inspire Graph Contrastive Learning","created":"2022-06-16","cited_by_count":57},{"id":"2109.03856","title":"Local Augmentation for Graph Neural Networks","created":"2021-09-08","cited_by_count":53},{"id":"2205.15117","title":"OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs","created":"2022-05-30","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":"2207.07888","title":"SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks","created":"2022-07-16","cited_by_count":37},{"id":"2203.14082","title":"Metropolis-Hastings Data Augmentation for Graph Neural Networks","created":"2022-03-26","cited_by_count":30},{"id":"2308.06714","title":"Learning on Graphs with Out-of-Distribution Nodes","created":"2023-08-13","cited_by_count":29},{"id":"2210.10253","title":"On the Adversarial Robustness of Mixture of Experts","created":"2022-10-19","cited_by_count":28},{"id":"2212.04983","title":"Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks","created":"2022-12-09","cited_by_count":21},{"id":"2302.02609","title":"Improving Domain Generalization with Domain Relations","created":"2023-02-06","cited_by_count":14},{"id":"2110.03865","title":"Stable Prediction on Graphs with Agnostic Distribution Shift","created":"2021-10-08","cited_by_count":9},{"id":"2206.09345","title":"Finding Diverse and Predictable Subgraphs for Graph Domain Generalization","created":"2022-06-19","cited_by_count":9},{"id":"2308.08344","title":"Graph Out-of-Distribution Generalization with Controllable Data Augmentation","created":"2023-08-16","cited_by_count":9},{"id":"1708.01236","title":"Multiscale mixing patterns in networks","created":"2017-08-03","cited_by_count":7}],"next_cursor":null}