A comprehensive survey on graph neural networks Z Wu, S Pan, F Chen, G Long, C Zhang, PS Yu. IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 32 (1), 4-24, 2020 | 5436 | 2020 |
Graph WaveNet for Deep Spatial-Temporal Graph Modeling Z Wu, S Pan, G Long, J Jiang, C Zhang International Joint Conference on Artificial Intelligence (IJCAI) 2019, 1907 …, 2019 | 889 | 2019 |
Disan: Directional self-attention network for rnn/cnn-free language understanding T Shen, T Zhou, G Long, J Jiang, S Pan, C Zhang AAAI Conference on Artificial Intelligence 2018, 5446-5455, 2018 | 724 | 2018 |
Adversarially Regularized Graph Autoencoder for Graph Embedding S Pan, R Hu, G Long, J Jiang, L Yao, C Zhang (IJCAI) International Joint Conference on Artificial Intelligence 2018, 2609 …, 2018 | 655 | 2018 |
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks Z Wu, S Pan, G Long, J Jiang, X Chang, C Zhang (SIGKDD) ACM SIG on Knowledge Discovery and Data Mining 2020, 753-763, 2020 | 507 | 2020 |
Mgae: Marginalized graph autoencoder for graph clustering C Wang, S Pan, G Long, X Zhu, J Jiang ACM on Conference on Information and Knowledge Management (CIKM), 889-898, 2017 | 289 | 2017 |
Attributed Graph Clustering: A Deep Attentional Embedding Approach C Wang, S Pan, R Hu, G Long, J Jiang, C Zhang International Joint Conference on Artificial Intelligence (IJCAI) 2019, 3670 …, 2019 | 264 | 2019 |
Learning graph embedding with adversarial training methods S Pan, R Hu, S Fung, G Long, J Jiang, C Zhang IEEE transactions on cybernetics (TCYB) 50 (6), 2475-2487, 2020 | 207 | 2020 |
Optimal cloud resource auto-scaling for web applications J Jiang, J Lu, G Zhang, G Long IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing …, 2013 | 189 | 2013 |
Bi-directional block self-attention for fast and memory-efficient sequence modeling T Shen, T Zhou, G Long, J Jiang, C Zhang (ICLR) International Conference on Learning Representations 2018, 1-18, 2018 | 156 | 2018 |
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling T Shen, T Zhou, G Long, J Jiang, S Wang, C Zhang International Joint Conference on Artificial Intelligence (IJCAI) 2018, 4345 …, 2018 | 134 | 2018 |
Supervised Learning for Suicidal Ideation Detection in Online User Content S Ji, CP Yu, S Fung, S Pan, G Long Complexity 2018, 6157249:1-6157249:10, 2018 | 130 | 2018 |
Scaling-up item-based collaborative filtering recommendation algorithm based on hadoop J Jiang, J Lu, G Zhang, G Long IEEE World Congress on Services (SERVICES), 490-497, 2011 | 130 | 2011 |
Suicidal ideation detection: A review of machine learning methods and applications S Ji, S Pan, X Li, E Cambria, G Long, Z Huang IEEE Transactions on Computational Social Systems 8 (1), 214-226, 2020 | 121 | 2020 |
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification W Tang, G Long, L Liu, T Zhou, M Blumenstein, J Jiang (ICLR) The Tenth International Conference on Learning Representations 2022, 2022 | 106* | 2022 |
Learning Private Neural Language Modeling with Attentive Aggregation S Ji, S Pan, G Long, X Li, J Jiang, Z Huang International Joint Conference on Neural Networks (IJCNN) 2019, 1--8, 2019 | 98 | 2019 |
Diagnosis code assignment using sparsity-based disease correlation embedding S Wang, X Chang, X Li, G Long, L Yao, Q Sheng IEEE Transactions on Knowledge & Data Engineering (TKDE) 28 (12), 3191-3202 …, 2016 | 88 | 2016 |
Federated Learning for Open Banking G Long, Y Tan, J Jiang, C Zhang Federated Learning: Privacy and Incentive, 240--254, 2020 | 87 | 2020 |
A Universal Representation Transformer Layer for Few-Shot Image Classification L Liu, W Hamilton, G Long, J Jiang, H Larochelle (ICLR) International Conference on Learning Representations 2021 (9), 1-11, 2021 | 85 | 2021 |
Learning to Propagate for Graph Meta-Learning L Liu, T Zhou, G Long, J Jiang, C Zhang The 33rd Conference on Neural Information Processing Systems (NeurIPS) 2019 …, 2019 | 85 | 2019 |