| Heterogeneous Graph Structure Learning for Graph Neural Networks J Zhao, X Wang, C Shi, B Hu, G Song, Y Ye AAAI conference on artificial intelligence (AAAI21), 2021 | 295 | 2021 |
| Network schema preserving heterogeneous information network embedding J Zhao, X Wang, C Shi, Z Liu, Y Ye International joint conference on artificial intelligence (IJCAI20), 2020 | 138 | 2020 |
| Learning on Large-scale Text-attributed Graphs via Variational Inference J Zhao, M Qu, C Li, H Yan, Q Liu, R Li, X Xie, J Tang Eleventh International Conference on Learning Representations (ICLR23 oral), 2022 | 133 | 2022 |
| Gophormer: Ego-graph transformer for node classification J Zhao, C Li, Q Wen, Y Wang, Y Liu, H Sun, X Xie, Y Ye arXiv preprint arXiv:2110.13094, 2021 | 83 | 2021 |
| GraphText: Graph Reasoning in Text Space J Zhao, L Zhuo, Y Shen, M Qu, K Liu, M Bronstein, Z Zhu, J Tang NeurIPS2024 Adaptive Foundation Models Workshop, 2023 | 69 | 2023 |
| Position: Graph Foundation Models Are Already Here H Mao, Z Chen, W Tang, J Zhao, Y Ma, T Zhao, N Shah, M Galkin, J Tang ICML 2024 (spotlight), 2024 | 58* | 2024 |
| HousE: Knowledge Graph Embedding with Householder Parameterization R Li, J Zhao, C Li, D He, Y Wang, Y Liu, H Sun, S Wang, W Deng, Y Shen, ... International Conference on Machine Learning (ICML22), 2022 | 58 | 2022 |
| Multi-view Self-supervised Heterogeneous Graph Embedding J Zhao, Q Wen, S Sun, Y Ye, C Zhang Machine Learning and Knowledge Discovery in Databases. Research European …, 2021 | 34 | 2021 |
| Adaptive Kernel Graph Neural Network M Ju, S Hou, Y Fan, J Zhao, Y Ye, L Zhao AAAI Conference on Artificial Intelligence (AAAI21), 2021 | 29 | 2021 |
| Self-Supervised Graph Structure Refinement for Graph Neural Networks J Zhao, Q Wen, M Ju, C Zhang, Y Ye ACM International Conference on Web Search and Data Mining (WSDM23), 2022 | 28 | 2022 |
| Improving relevance modeling via heterogeneous behavior graph learning in bing ads B Pang, C Li, Y Liu, J Lian, J Zhao, H Sun, W Deng, X Xie, Q Zhang ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD22), 2022 | 21 | 2022 |
| Test-time training for graph neural networks Y Wang, C Li, W Jin, R Li, J Zhao, J Tang, X Xie DASFAA 2025, 2025 | 18 | 2025 |
| A comprehensive study on text-attributed graphs: Benchmarking and rethinking H Yan, C Li, R Long, C Yan, J Zhao, W Zhuang, J Yin, P Zhang, W Han, ... NeurIPS 2023, 2023 | 15 | 2023 |
| Fully-inductive Node Classification on Arbitrary Graphs J Zhao, M Galkin, H Mostafa, MM Bronstein, Z Zhu, J Tang ICLR 2025, 2024 | 12* | 2024 |
| Rxnet: Rx-refill graph neural network for overprescribing detection J Zhang, AT Kuo, J Zhao, Q Wen, E Winstanley, C Zhang, Y Ye ACM International Conference on Information & Knowledge Management (CIKM21), 2021 | 11 | 2021 |
| Prohibited item detection via risk graph structure learning Y Ji, G Chu, X Wang, C Shi, J Zhao, J Du The ACM Web Conference (WWW22), 2022 | 10 | 2022 |
| To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion R Li, X Chen, C Li, Y Shen, J Zhao, Y Wang, W Han, H Sun, W Deng, ... ACL 2023 (main conference), 2023 | 7 | 2023 |
| Coupled semi-supervised clustering: Exploring attribute correlations in heterogeneous information networks J Zhao, D Xiao, L Hu, C Shi Web and Big Data: Third International Joint Conference, APWeb-WAIM 2019 …, 2019 | 3 | 2019 |
| Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study H Du, J Zhao, Y Zhao, S Xu, X Lin, Y Chen, LM Gardner, HF Yang arXiv preprint arXiv:2404.06962, 2024 | 1 | 2024 |
| Rx-refill graph neural network to reduce drug overprescribing risks J Zhang, AT Kuo, J Zhao, Q Wen, EL Winstanley, C Zhang, Y Ye IJCAI, 5379-5383, 2022 | 1 | 2022 |
Yanfang (Fanny) YeGalassi Family Collegiate Professor in Computer Science and Engineering, University of Notre DameVahvistettu sähköpostiosoite verkkotunnuksessa nd.edu