{"list":"other","total":65,"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":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"2005.00687","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","created":"2020-05-02","cited_by_count":2163},{"id":"1703.06103","title":"Modeling Relational Data with Graph Convolutional Networks","created":"2017-03-17","cited_by_count":1694},{"id":"2010.13902","title":"Graph Contrastive Learning with Augmentations","created":"2020-10-22","cited_by_count":1208},{"id":"1809.10341","title":"Deep Graph Infomax","created":"2018-09-27","cited_by_count":1106},{"id":"1801.07606","title":"Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning","created":"2018-01-22","cited_by_count":1102},{"id":"1802.04712","title":"Attention-based Deep Multiple Instance Learning","created":"2018-02-13","cited_by_count":1095},{"id":"1904.12848","title":"Unsupervised Data Augmentation for Consistency Training","created":"2019-04-29","cited_by_count":1060},{"id":"1903.07293","title":"Heterogeneous Graph Attention Network","created":"2019-03-18","cited_by_count":734},{"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":"2003.01332","title":"Heterogeneous Graph Transformer","created":"2020-03-03","cited_by_count":582},{"id":"1911.06455","title":"Graph Transformer Networks","created":"2019-11-06","cited_by_count":523},{"id":"2011.08939","title":"Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning","created":"2020-11-17","cited_by_count":478},{"id":"2111.14522","title":"Understanding over-squashing and bottlenecks on graphs via curvature","created":"2021-11-29","cited_by_count":478},{"id":"2006.09963","title":"GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training","created":"2020-06-17","cited_by_count":463},{"id":"1802.03480","title":"GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders","created":"2018-02-09","cited_by_count":383},{"id":"1905.07854","title":"KGAT: Knowledge Graph Attention Network for Recommendation","created":"2019-05-20","cited_by_count":343},{"id":"2110.07875","title":"Graph Neural Networks with Learnable Structural and Positional Representations","created":"2021-10-15","cited_by_count":333},{"id":"2006.15437","title":"GPT-GNN: Generative Pre-Training of Graph Neural Networks","created":"2020-06-27","cited_by_count":303},{"id":"2110.07178","title":"Symbolic Knowledge Distillation: from General Language Models to Commonsense Models","created":"2021-10-14","cited_by_count":270},{"id":"1903.11960","title":"Learning Discrete Structures for Graph Neural Networks","created":"2019-03-28","cited_by_count":231},{"id":"2310.13023","title":"GraphGPT: Graph Instruction Tuning for Large Language Models","created":"2023-10-19","cited_by_count":226},{"id":"2005.11079","title":"Graph Random Neural Network for Semi-Supervised Learning on Graphs","created":"2020-05-22","cited_by_count":213},{"id":"2106.05819","title":"Adversarial Graph Augmentation to Improve Graph Contrastive Learning","created":"2021-06-10","cited_by_count":200},{"id":"2311.00423","title":"LLMRec: Large Language Models with Graph Augmentation for Recommendation","created":"2023-11-01","cited_by_count":172},{"id":"2201.08860","title":"GreaseLM: Graph REASoning Enhanced Language Models for Question Answering","created":"2022-01-21","cited_by_count":171},{"id":"2010.12811","title":"Graph Information Bottleneck","created":"2020-10-24","cited_by_count":168},{"id":"1611.07012","title":"GRAM: Graph-based Attention Model for Healthcare Representation Learning","created":"2016-11-21","cited_by_count":156},{"id":"2210.14709","title":"Learning on Large-scale Text-attributed Graphs via Variational Inference","created":"2022-10-26","cited_by_count":155},{"id":"2103.08826","title":"GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks","created":"2021-03-16","cited_by_count":147},{"id":"1711.10730","title":"Heterogeneous Information Network Embedding for Recommendation","created":"2017-11-29","cited_by_count":133},{"id":"2202.08235","title":"Data Augmentation for Deep Graph Learning: A Survey","created":"2022-02-16","cited_by_count":133},{"id":"2305.19523","title":"Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning","created":"2023-05-31","cited_by_count":133},{"id":"2006.09136","title":"When Does Self-Supervision Help Graph Convolutional Networks?","created":"2020-06-16","cited_by_count":121},{"id":"1706.05764","title":"Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks","created":"2017-06-19","cited_by_count":116},{"id":"1902.08226","title":"Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure","created":"2019-02-20","cited_by_count":93},{"id":"1810.09593","title":"MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare","created":"2018-10-22","cited_by_count":72},{"id":"2302.08191","title":"LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation","created":"2023-02-16","cited_by_count":72},{"id":"1911.12216","title":"ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context","created":"2019-11-27","cited_by_count":56},{"id":"2105.02711","title":"SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinations","created":"2021-05-05","cited_by_count":54},{"id":"2109.03856","title":"Local Augmentation for Graph Neural Networks","created":"2021-09-08","cited_by_count":53},{"id":"1911.12205","title":"AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration","created":"2019-11-27","cited_by_count":51},{"id":"2001.10054","title":"StageNet: Stage-Aware Neural Networks for Health Risk Prediction","created":"2020-01-24","cited_by_count":48},{"id":"2007.10306","title":"An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction","created":"2020-07-20","cited_by_count":45},{"id":"2403.12025","title":"A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models","created":"2024-03-18","cited_by_count":45},{"id":"2105.01876","title":"Change Matters: Medication Change Prediction with Recurrent Residual Networks","created":"2021-05-05","cited_by_count":38},{"id":"2101.06800","title":"Heterogeneous Similarity Graph Neural Network on Electronic Health Records","created":"2021-01-17","cited_by_count":30},{"id":"2111.05639","title":"Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation","created":"2021-11-10","cited_by_count":30},{"id":"2203.14082","title":"Metropolis-Hastings Data Augmentation for Graph Neural Networks","created":"2022-03-26","cited_by_count":30},{"id":"1905.13728","title":"Pre-Training Graph Neural Networks for Generic Structural Feature Extraction","created":"2019-05-31","cited_by_count":24},{"id":"2205.09802","title":"Label-invariant Augmentation for Semi-Supervised Graph Classification","created":"2022-05-19","cited_by_count":19},{"id":"2309.12971","title":"Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes","created":"2023-09-22","cited_by_count":15},{"id":"2302.03248","title":"Disentangled Causal Embedding With Contrastive Learning For Recommender System","created":"2023-02-07","cited_by_count":10},{"id":"2101.04209","title":"PyHealth: A Python Library for Health Predictive Models","created":"2021-01-11","cited_by_count":9},{"id":"2210.15901","title":"Mitigating Health Disparities in EHR via Deconfounder","created":"2022-10-28","cited_by_count":7},{"id":"2307.04336","title":"Source-Aware Embedding Training on Heterogeneous Information Networks","created":"2023-07-10","cited_by_count":5},{"id":"1812.06229","title":"Domain-to-Domain Translation Model for Recommender System","created":"2018-12-15","cited_by_count":2},{"id":"2308.11819","title":"A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder","created":"2023-08-22","cited_by_count":1}],"next_cursor":null}