Dongxiao He
Dongxiao He
Tianjin University, Professor of Computer Science
Verified email at - Homepage
Cited by
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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
Modularity based community detection with deep learning.
L Yang, X Cao, D He, C Wang, X Wang, W Zhang
IJCAI 16 (2016), 2252-2258, 2016
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
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
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
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
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
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
Universal graph convolutional networks
D Jin, Z Yu, C Huo, R Wang, X Wang, D He, J Han
Advances in Neural Information Processing Systems 34, 10654-10664, 2021
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
Powerful graph convolutional networks with adaptive propagation mechanism for homophily and heterophily
T Wang, D Jin, R Wang, D He, Y Huang
Proceedings of the AAAI conference on artificial intelligence 36 (4), 4210-4218, 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
Block modeling-guided graph convolutional neural networks
D He, C Liang, H Liu, M Wen, P Jiao, Z Feng
Proceedings of the AAAI conference on artificial intelligence 36 (4), 4022-4029, 2022
AS-GCN: Adaptive semantic architecture of graph convolutional networks for text-rich networks
Z Yu, D Jin, Z Liu, D He, X Wang, H Tong, J Han
2021 IEEE International Conference on Data Mining (ICDM), 837-846, 2021
Temporal network embedding for link prediction via VAE joint attention mechanism
P Jiao, X Guo, X Jing, D He, H Wu, S Pan, M Gong, W Wang
IEEE Transactions on Neural Networks and Learning Systems 33 (12), 7400-7413, 2021
Community mining in complex networks clustering combination based genetic algorithm
DX He, X Zhou, Z Wang, CG Zhou, Z Wang, D Jin
Acta Automatica Sinica 36 (8), 1160-1170, 2010
Genetic algorithm with local search for community detection in large-scale complex networks
D Jin, J Liu, B Yang, DX He, DY Liu
Acta Automatica Sinica 37 (7), 873-882, 2011
Identification of hybrid node and link communities in complex networks
D He, D Jin, Z Chen, W Zhang
Scientific reports 5 (1), 8638, 2015
Event prediction based on evolutionary event ontology knowledge
Q Mao, X Li, H Peng, J Li, D He, S Guo, M He, L Wang
Future Generation Computer Systems 115, 76-89, 2021
Modeling with node degree preservation can accurately find communities
D Jin, Z Chen, D He, W Zhang
Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015
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