| Topological recurrent neural network for diffusion prediction J Wang, VW Zheng, Z Liu, KCC Chang Proceedings of The IEEE International Conference on Data Mining (ICDM), 2017 | 198 | 2017 |
| Graphprompt: Unifying pre-training and downstream tasks for graph neural networks Z Liu, X Yu, Y Fang, X Zhang Proceedings of the ACM web conference 2023, 417-428, 2023 | 172 | 2023 |
| Tail-GNN: Tail-node graph neural networks Z Liu, TK Nguyen, Y Fang Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021 | 123 | 2021 |
| Relative and absolute location embedding for few-shot node classification on graph Z Liu, Y Fang, C Liu, SCH Hoi Proceedings of the AAAI conference on artificial intelligence 35 (5), 4267-4275, 2021 | 94 | 2021 |
| Semantic proximity search on heterogeneous graph by proximity embedding Z Liu, VW Zheng, Z Zhao, F Zhu, KCC Chang, M Wu, J Ying Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 87 | 2017 |
| Towards locality-aware meta-learning of tail node embeddings on networks Z Liu, W Zhang, Y Fang, X Zhang, SCH Hoi Proceedings of the 29th ACM international conference on information …, 2020 | 61 | 2020 |
| Hinormer: Representation learning on heterogeneous information networks with graph transformer Q Mao, Z Liu, C Liu, J Sun Proceedings of the ACM web conference 2023, 599-610, 2023 | 60 | 2023 |
| mg2vec: Learning relationship-preserving heterogeneous graph representations via metagraph embedding W Zhang, Y Fang, Z Liu, M Wu, X Zhang IEEE Transactions on Knowledge and Data Engineering 34 (3), 1317-1329, 2020 | 58 | 2020 |
| Distance-aware dag embedding for proximity search on heterogeneous graphs Z Liu, V Zheng, Z Zhao, F Zhu, K Chang, M Wu, J Ying Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 42 | 2018 |
| Interactive paths embedding for semantic proximity search on heterogeneous graphs Z Liu, VW Zheng, Z Zhao, Z Li, H Yang, M Wu, J Ying Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 40 | 2018 |
| On Generalized Degree Fairness in Graph Neural Networks Z Liu, TK Nguyen, Y Fang Proceedings of the AAAI Conference on Artificial Intelligence 37 (1), 2023 | 39 | 2023 |
| Meta-inductive node classification across graphs Z Wen, Y Fang, Z Liu Proceedings of the 44th international ACM SIGIR conference on research and …, 2021 | 39 | 2021 |
| Hgprompt: Bridging homogeneous and heterogeneous graphs for few-shot prompt learning X Yu, Y Fang, Z Liu, X Zhang Proceedings of the AAAI conference on artificial intelligence 38 (15), 16578 …, 2024 | 38 | 2024 |
| Cooperative Explanations of Graph Neural Networks J Fang, X Wang, A Zhang, Z Liu, X He, TS Chua Proceedings of the 16th ACM International Conference on Web Search and Data …, 2023 | 34 | 2023 |
| Evaluating post-hoc explanations for graph neural networks via robustness analysis J Fang, W Liu, Y Gao, Z Liu, A Zhang, X Wang, X He Advances in neural information processing systems 36, 72446-72463, 2023 | 32 | 2023 |
| Subgraph-augmented path embedding for semantic user search on heterogeneous social network Z Liu, VW Zheng, Z Zhao, H Yang, KCC Chang, M Wu, J Ying Proceedings of the 2018 World Wide Web Conference, 1613-1622, 2018 | 32 | 2018 |
| Learning to count isomorphisms with graph neural networks X Yu, Z Liu, Y Fang, X Zhang Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 4845-4853, 2023 | 28 | 2023 |
| Node-wise localization of graph neural networks Z Liu, Y Fang, C Liu, SCH Hoi International Joint Conferences on Artificial Intelligence, 2021 | 27 | 2021 |
| Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs X Yu, Z Liu, Y Fang, Z Liu, S Chen, X Zhang IEEE Transactions on Knowledge and Data Engineering, 2024 | 25 | 2024 |
| On size-oriented long-tailed graph classification of graph neural networks Z Liu, Q Mao, C Liu, Y Fang, J Sun Proceedings of the ACM web conference 2022, 1506-1516, 2022 | 23 | 2022 |
Yuan FANGSingapore Management UniversityAdresă de e-mail confirmată pe smu.edu.sg