| A comprehensive survey on graph anomaly detection with deep learning X Ma, J Wu, S Xue, J Yang, C Zhou, QZ Sheng, H Xiong, L Akoglu IEEE Transactions on Knowledge and Data Engineering, 2021 | 485 | 2021 |
| Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp JX Morris, E Lifland, JY Yoo, J Grigsby, D Jin, Y Qi arXiv preprint arXiv:2005.05909, 2020 | 412 | 2020 |
| A unified semi-supervised community detection framework using latent space graph regularization L Yang, X Cao, D Jin, X Wang, D Meng IEEE transactions on cybernetics 45 (11), 2585-2598, 2014 | 205 | 2014 |
| A survey of community detection approaches: From statistical modeling to deep learning D Jin, Z Yu, P Jiao, S Pan, D He, J Wu, SY Philip, W Zhang IEEE Transactions on Knowledge and Data Engineering 35 (2), 1149-1170, 2021 | 195 | 2021 |
| Semantic community identification in large attribute networks X Wang, D Jin, X Cao, L Yang, W Zhang Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 193 | 2016 |
| Complex network clustering algorithms 杨博， 刘大有LIU Journal of software 20 (1), 54-66, 2009 | 149 | 2009 |
| Heterogeneous graph neural network via attribute completion D Jin, C Huo, C Liang, L Yang Proceedings of the web conference 2021, 391-400, 2021 | 114 | 2021 |
| Graph convolutional networks meet markov random fields: Semi-supervised community detection in attribute networks D Jin, Z Liu, W Li, D He, W Zhang Proceedings of the AAAI conference on artificial intelligence 33 (01), 152-159, 2019 | 109 | 2019 |
| Joint identification of network communities and semantics via integrative modeling of network topologies and node contents D He, Z Feng, D Jin, X Wang, W Zhang Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 104 | 2017 |
| Identifying overlapping communities as well as hubs and outliers via nonnegative matrix factorization X Cao, X Wang, D Jin, Y Cao, D He Scientific reports 3 (1), 2993, 2013 | 99 | 2013 |
| A Markov random walk under constraint for discovering overlapping communities in complex networks D Jin, B Yang, C Baquero, D Liu, D He, J Liu Journal of Statistical Mechanics: Theory and Experiment 2011 (05), P05031, 2011 | 99 | 2011 |
| What disease does this patient have? a large-scale open domain question answering dataset from medical exams D Jin, E Pan, N Oufattole, WH Weng, H Fang, P Szolovits Applied Sciences 11 (14), 6421, 2021 | 94 | 2021 |
| Graph neural networks for graphs with heterophily: A survey X Zheng, Y Liu, S Pan, M Zhang, D Jin, PS Yu arXiv preprint arXiv:2202.07082, 2022 | 79 | 2022 |
| Genetic algorithm with local search for community mining in complex networks D Jin, D He, D Liu, C Baquero 2010 22nd IEEE international conference on tools with artificial …, 2010 | 79 | 2010 |
| Topology Optimization based Graph Convolutional Network. L Yang, Z Kang, X Cao, D Jin, B Yang, Y Guo IJCAI, 4054-4061, 2019 | 77 | 2019 |
| Adaptive community detection incorporating topology and content in social networks M Qin, D Jin, D He, B Gabrys, K Musial Proceedings of the 2017 IEEE/ACM International Conference on Advances in …, 2017 | 77 | 2017 |
| Semi-supervised community detection based on non-negative matrix factorization with node popularity X Liu, W Wang, D He, P Jiao, D Jin, CV Cannistraci Information Sciences 381, 304-321, 2017 | 72 | 2017 |
| Community-centric graph convolutional network for unsupervised community detection D He, Y Song, D Jin, Z Feng, B Zhang, Z Yu, W Zhang Proceedings of the twenty-ninth international conference on international …, 2021 | 68 | 2021 |
| Incorporating network structure with node contents for community detection on large networks using deep learning J Cao, D Jin, L Yang, J Dang Neurocomputing 297, 71-81, 2018 | 59 | 2018 |
| Combined node and link partitions method for finding overlapping communities in complex networks D Jin, B Gabrys, J Dang Scientific reports 5 (1), 8600, 2015 | 55 | 2015 |
Dongxiao HeTianjin University, Associate Professor of Computer ScienceVerified email at tju.edu.cn
