| Easing embedding learning by comprehensive transcription of heterogeneous information networks Y Shi, Q Zhu, F Guo, C Zhang, J Han Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 150 | 2018 |
| Transfer learning of graph neural networks with ego-graph information maximization Q Zhu, C Yang, Y Xu, H Wang, C Zhang, J Han Advances in Neural Information Processing Systems 34, 2021 | 126 | 2021 |
| AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks Y Shi, H Gui, Q Zhu, L Kaplan, J Han Proceedings of the 2018 SIAM International Conference on Data Mining, 144-152, 2018 | 116 | 2018 |
| Shift-robust gnns: Overcoming the limitations of localized graph training data Q Zhu, N Ponomareva, J Han, B Perozzi Advances in Neural Information Processing Systems 34, 27965-27977, 2021 | 115 | 2021 |
| Heterogeneous supervision for relation extraction: A representation learning approach L Liu, X Ren, Q Zhu, S Zhi, H Gui, H Ji, J Han arXiv preprint arXiv:1707.00166, 2017 | 85 | 2017 |
| Collective multi-type entity alignment between knowledge graphs Q Zhu, H Wei, B Sisman, D Zheng, C Faloutsos, XL Dong, J Han Proceedings of The Web Conference 2020, 2241-2252, 2020 | 63 | 2020 |
| Patton: Language model pretraining on text-rich networks B Jin, W Zhang, Y Zhang, Y Meng, X Zhang, Q Zhu, J Han arXiv preprint arXiv:2305.12268, 2023 | 41 | 2023 |
| Can Single-Pass Contrastive Learning Work for Both Homophilic and Heterophilic Graph? H Wang, J Zhang, Q Zhu, W Huang | 37* | 2022 |
| Heterformer: Transformer-based deep node representation learning on heterogeneous text-rich networks B Jin, Y Zhang, Q Zhu, J Han Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 35* | 2023 |
| Task-guided pair embedding in heterogeneous network C Park, D Kim, Q Zhu, J Han, H Yu Proceedings of the 28th ACM international conference on information and …, 2019 | 34 | 2019 |
| Unsupervised differentiable multi-aspect network embedding C Park, C Yang, Q Zhu, D Kim, H Yu, J Han Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 32 | 2020 |
| Life-inet: A structured network-based knowledge exploration and analytics system for life sciences X Ren, J Shen, M Qu, X Wang, Z Wu, Q Zhu, M Jiang, F Tao, S Sinha, ... Proceedings of ACL 2017, System Demonstrations, 55-60, 2017 | 30 | 2017 |
| Can GNN be Good Adapter for LLMs? X Huang, K Han, Y Yang, D Bao, Q Tao, Z Chai, Q Zhu Proceedings of the ACM on Web Conference 2024, 893-904, 2024 | 29* | 2024 |
| The effect of metadata on scientific literature tagging: A cross-field cross-model study Y Zhang, B Jin, Q Zhu, Y Meng, J Han Proceedings of the ACM Web Conference 2023, 1626-1637, 2023 | 22 | 2023 |
| Discovering hypernymy in text-rich heterogeneous information network by exploiting context granularity Y Shi, J Shen, Y Li, N Zhang, X He, Z Lou, Q Zhu, M Walker, M Kim, J Han Proceedings of the 28th ACM International Conference on Information and …, 2019 | 20 | 2019 |
| Facet-aware evaluation for extractive summarization Y Mao, L Liu, Q Zhu, X Ren, J Han arXiv preprint arXiv:1908.10383, 2019 | 18 | 2019 |
| Expert finding in heterogeneous bibliographic networks with locally-trained embeddings H Gui, Q Zhu, L Liu, A Zhang, J Han arXiv preprint arXiv:1803.03370, 2018 | 18 | 2018 |
| Integrating local context and global cohesiveness for open information extraction Q Zhu, X Ren, J Shang, Y Zhang, A El-Kishky, J Han Proceedings of the Twelfth ACM International Conference on Web Search and …, 2019 | 17* | 2019 |
| Explaining and adapting graph conditional shift Q Zhu, Y Jiao, N Ponomareva, J Han, B Perozzi arXiv preprint arXiv:2306.03256, 2023 | 14 | 2023 |
| Shift-Robust Node Classification via Graph Clustering Co-training Q Zhu, C Zhang, C Park, C Yang, J Han NeurIPS 2022 Workshop: New Frontiers in Graph Learning, 2022 | 12* | 2022 |
Jiawei HanAbel Bliss Professor of Computer Science, University of IllinoisVerified email at cs.uiuc.edu
