| A comprehensive survey on graph neural networks Z Wu, S Pan, F Chen, G Long, C Zhang, SY Philip IEEE transactions on neural networks and learning systems 32 (1), 4-24, 2020 | 9950 | 2020 |
| Graph wavenet for deep spatial-temporal graph modeling Z Wu, S Pan, G Long, J Jiang, C Zhang arXiv preprint arXiv:1906.00121, 2019 | 2112 | 2019 |
| Connecting the dots: Multivariate time series forecasting with graph neural networks Z Wu, S Pan, G Long, J Jiang, X Chang, C Zhang Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 1394 | 2020 |
| Adversarially regularized graph autoencoder for graph embedding S Pan, R Hu, G Long, J Jiang, L Yao, C Zhang arXiv preprint arXiv:1802.04407, 2018 | 1031 | 2018 |
| Data preparation for data mining S Zhang, C Zhang, Q Yang Applied artificial intelligence 17 (5-6), 375-381, 2003 | 893 | 2003 |
| Disan: Directional self-attention network for rnn/cnn-free language understanding T Shen, T Zhou, G Long, J Jiang, S Pan, C Zhang Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 885 | 2018 |
| Association Rule Mining: Models and Algorithms C. Zhang, and S. Zhang Lecture Notes in Computer Science LNAI 2307, 2002 | 859* | 2002 |
| Efficient mining of both positive and negative association rules X Wu, C Zhang, S Zhang ACM Transactions on Information Systems (TOIS) 22 (3), 381-405, 2004 | 844 | 2004 |
| Network representation learning: A survey D Zhang, J Yin, X Zhu, C Zhang IEEE transactions on Big Data 6 (1), 3-28, 2018 | 797 | 2018 |
| Attributed graph clustering: A deep attentional embedding approach C Wang, S Pan, R Hu, G Long, J Jiang, C Zhang arXiv preprint arXiv:1906.06532, 2019 | 534 | 2019 |
| Tri-party deep network representation S Pan, J Wu, X Zhu, C Zhang, Y Wang International Joint Conference on Artificial Intelligence 2016, 1895-1901, 2016 | 529 | 2016 |
| Fedproto: Federated prototype learning across heterogeneous clients Y Tan, G Long, L Liu, T Zhou, Q Lu, J Jiang, C Zhang Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8432-8440, 2022 | 403 | 2022 |
| Learning graph embedding with adversarial training methods S Pan, R Hu, S Fung, G Long, J Jiang, C Zhang IEEE transactions on cybernetics 50 (6), 2475-2487, 2019 | 330 | 2019 |
| Federated learning for open banking G Long, Y Tan, J Jiang, C Zhang Federated learning: privacy and incentive, 240-254, 2020 | 293 | 2020 |
| Dynamic affinity graph construction for spectral clustering using multiple features Z Li, F Nie, X Chang, Y Yang, C Zhang, N Sebe IEEE transactions on neural networks and learning systems 29 (12), 6323-6332, 2018 | 271 | 2018 |
| Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support X Yan, C Zhang, S Zhang Expert Systems with Applications 36 (2), 3066-3076, 2009 | 267 | 2009 |
| Compound Rank- Projections for Bilinear Analysis X Chang, F Nie, S Wang, Y Yang, X Zhou, C Zhang IEEE transactions on neural networks and learning systems 27 (7), 1502-1513, 2015 | 230 | 2015 |
| Support vector machines based on K-means clustering for real-time business intelligence systems J Wang, X Wu, C Zhang International Journal of Business Intelligence and Data Mining 1 (1), 54-64, 2005 | 186 | 2005 |
| Probabilistic exposure fusion M Song, D Tao, C Chen, J Bu, J Luo, C Zhang IEEE Transactions on Image Processing 21 (1), 341-357, 2011 | 183 | 2011 |
| Bi-directional block self-attention for fast and memory-efficient sequence modeling T Shen, T Zhou, G Long, J Jiang, C Zhang arXiv preprint arXiv:1804.00857, 2018 | 177 | 2018 |
Guodong LongAssociate Professor, Faculty of Engineering and IT, University of Technology SydneyVerified email at uts.edu.au
