| Heterogeneous graph attention network X Wang, H Ji, C Shi, B Wang, Y Ye, P Cui, PS Yu The world wide web conference, 2022-2032, 2019 | 2900 | 2019 |
| A survey of heterogeneous information network analysis C Shi, Y Li, J Zhang, Y Sun, SY Philip IEEE Transactions on Knowledge and Data Engineering 29 (1), 17-37, 2016 | 1241 | 2016 |
| Heterogeneous information network embedding for recommendation C Shi, B Hu, WX Zhao, SY Philip IEEE transactions on knowledge and data engineering 31 (2), 357-370, 2018 | 1229 | 2018 |
| Leveraging meta-path based context for top-n recommendation with a neural co-attention model B Hu, C Shi, WX Zhao, PS Yu Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 786 | 2018 |
| Beyond low-frequency information in graph convolutional networks D Bo, X Wang, C Shi, H Shen Proceedings of the AAAI conference on artificial intelligence 35 (5), 3950-3957, 2021 | 649 | 2021 |
| Structural deep clustering network D Bo, X Wang, C Shi, M Zhu, E Lu, P Cui Proceedings of the web conference 2020, 1400-1410, 2020 | 584 | 2020 |
| Am-gcn: Adaptive multi-channel graph convolutional networks X Wang, M Zhu, D Bo, P Cui, C Shi, J Pei Proceedings of the 26th ACM SIGKDD International conference on knowledge …, 2020 | 550 | 2020 |
| Heterogeneous graph attention networks for semi-supervised short text classification H Linmei, T Yang, C Shi, H Ji, X Li Proceedings of the 2019 conference on empirical methods in natural language …, 2019 | 430 | 2019 |
| A survey on heterogeneous graph embedding: methods, techniques, applications and sources X Wang, D Bo, C Shi, S Fan, Y Ye, SY Philip IEEE Transactions on Big Data 9 (2), 415-436, 2022 | 416 | 2022 |
| Self-supervised heterogeneous graph neural network with co-contrastive learning X Wang, N Liu, H Han, C Shi Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 411 | 2021 |
| Metapath-guided heterogeneous graph neural network for intent recommendation S Fan, J Zhu, X Han, C Shi, L Hu, B Ma, Y Li Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 408 | 2019 |
| Semantic path based personalized recommendation on weighted heterogeneous information networks C Shi, Z Zhang, P Luo, PS Yu, Y Yue, B Wu Proceedings of the 24th ACM international on conference on information and …, 2015 | 346 | 2015 |
| Hetesim: A general framework for relevance measure in heterogeneous networks C Shi, X Kong, Y Huang, SY Philip, B Wu IEEE Transactions on Knowledge and Data Engineering 26 (10), 2479-2492, 2014 | 330 | 2014 |
| Meta-learning on heterogeneous information networks for cold-start recommendation Y Lu, Y Fang, C Shi Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 308 | 2020 |
| Heterogeneous graph structure learning for graph neural networks J Zhao, X Wang, C Shi, B Hu, G Song, Y Ye Proceedings of the AAAI conference on artificial intelligence 35 (5), 4697-4705, 2021 | 299 | 2021 |
| Adversarial learning on heterogeneous information networks B Hu, Y Fang, C Shi Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 245 | 2019 |
| One2multi graph autoencoder for multi-view graph clustering S Fan, X Wang, C Shi, E Lu, K Lin, B Wang proceedings of the web conference 2020, 3070-3076, 2020 | 230 | 2020 |
| Graph neural news recommendation with long-term and short-term interest modeling L Hu, C Li, C Shi, C Yang, C Shao Information Processing & Management 57 (2), 102142, 2020 | 221 | 2020 |
| Interpreting and unifying graph neural networks with an optimization framework M Zhu, X Wang, C Shi, H Ji, P Cui Proceedings of the web conference 2021, 1215-1226, 2021 | 218 | 2021 |
| Multi-objective community detection in complex networks C Shi, Z Yan, Y Cai, B Wu Applied Soft Computing 12 (2), 850-859, 2012 | 218 | 2012 |
Philip S. YuProfessor of Computer Science, University of Illinons at ChicagoEmail yang diverifikasi di cs.uic.edu