Learning Deep Network Representations with Adversarially Regularized Autoencoders
Open accessAbstract
The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure. Most existing network embedding models, with shallow or deep architectures, learn vertex representations from the sampled vertex sequences such that the low-dimensional embeddings preserve the locality property and/or global reconstruction capability. The resultant representations, however, are difficult for model generalization due to the intrinsic sparsity of sampled sequences from the input network. As such, an ideal approach to address the problem is to generate vertex representations by learning a probability density function over the sampled sequences. However, in many cases, such a distribution in a low-dimensional manifold may not always have an analytic form. In this study, we propose to learn the network representations with adversarially regularized autoencoders (NetRA). NetRA learns smoothly regularized vertex representations that well capture the network structure through jointly considering both locality-preserving and global reconstruction constraints. The joint inference is encapsulated in a generative adversarial training process to circumvent the requirement of an explicit prior distribution, and thus obtains better generalization performance. We demonstrate empirically how well key properties of the network structure are captured and the effectiveness of NetRA on a variety of tasks, including network reconstruction, link prediction, and multi-label classification.
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Cites
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235 · see citing works
(2016) · Cited by 11,679 · see citing works
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926 · see citing works
Inductive Representation Learning on Large Graphs
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1706.02216 · Cited by 10,639 · see citing works
DeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123 · see citing works
Advances In Deep Learning On Graphs (Gsp'18 Workshop)Preprint
Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.09375 · Cited by 6,912 · see citing works
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800 · see citing works
LIBLINEAR: A Library for Large Linear Classification
Fan, R.-E., Chang, K., Hsieh, C., Xiang-rui, W., & Lin, C. (2008) · doi:10.5555/1390681.1442794 · Cited by 6,680 · see citing works
(2017) · International Conference on Machine Learning · Cited by 6,382 · see citing works
LINE: Large-scale Information Network Embedding
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741093 · Cited by 5,416 · see citing works
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,977 · see citing works
Optimal Transport: Old and New
Villani, C. (2013) · Grundlehren der mathematischen Wissenschaften · doi:10.1007/978-3-540-71050-9 · Cited by 3,838 · see citing works
Feature-rich part-of-speech tagging with a cyclic dependency network
Toutanova, K., Klein, D., Manning, C. D., & Singer, Y. (2003) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1073445.1073478 · Cited by 2,889 · see citing works
Structural Deep Network Embedding
Wang, D., Cui, P., & Zhu, W. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939753 · Cited by 2,845 · see citing works
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Yu, L., Zhang, W., Wang, Y.-X., & Yu, Y. H. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10804 · Cited by 2,342 · see citing works
Dong, Y., Chawla, N. V., & Swami, A. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098036 · Cited by 2,224 · see citing works
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015) · arXiv · doi:10.48550/arxiv.1511.05644 · Cited by 1,579 · see citing works
Improving Distributional Similarity with Lessons Learned from Word Embeddings
Levy, O., Goldberg, Y., & Dagan, I. (2015) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00134 · Cited by 1,307 · see citing works
Envelope Theorems for Arbitrary Choice Sets
Milgrom, P., & Segal, I. (2002) · Econometrica · doi:10.1111/1468-0262.00296 · Cited by 1,295 · see citing works
Ribeiro, L. F. R., Saverese, P. H. P., & Figueiredo, D. R. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098061 · Cited by 1,153 · see citing works
Deep Neural Networks for Learning Graph Representations
Cao, S., Lu, W., & Xu, Q. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10179 · Cited by 1,070 · see citing works
Clustering in weighted networks
Opsahl, T., & Panzarasa, P. (2009) · Social Networks · doi:10.1016/j.socnet.2009.02.002 · Cited by 1,037 · see citing works
Relational learning via latent social dimensions
Tang, L., & Liu, H. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557109 · Cited by 799 · see citing works
The BioGRID Interaction Database: 2008 update
Breitkreutz, B., Stark, C., Reguly, T., Boucher, L., Breitkreutz, A., Livstone, M. S., Oughtred, R., Lackner, D. H., Bähler, J., Wood, V., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm1001 · Cited by 698 · see citing works
GraphGAN: Graph Representation Learning With Generative Adversarial Nets
Wang, H., Wang, J., Wang, J., Zhao, M., Zhang, W., Zhang, F., Xie, X., & Guo, M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11872 · Cited by 689 · see citing works
Learning Deep Representations for Graph Clustering
Tian, F., Gao, B., Cui, Q., Chen, E., & Liu, T. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8916 · Cited by 680 · see citing works
Label Informed Attributed Network Embedding
Huang, X. X., Li, J., & Hu, X. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018667 · Cited by 498 · see citing works
Network visualization and analysis of gene expression data using BioLayout Express3D
Theocharidis, A., van Dongen, S., Enright, A. J., & Freeman, T. C. (2009) · Nature Protocols · doi:10.1038/nprot.2009.177 · Cited by 453 · see citing works
Deep learning via semi-supervised embedding
Weston, J., Ratle, F., & Collobert, R. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390303 · Cited by 380 · see citing works
Leveraging social media networks for classification
Tang, L., & Liu, H. (2011) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0210-x · Cited by 360 · see citing works
Towards time-aware link prediction in evolving social networks
Tylenda, T., Angelova, R., & Bedathur, S. (2009) · doi:10.1145/1731011.1731020 · Cited by 216 · see citing works
Maximum-Likelihood Augmented Discrete Generative Adversarial NetworksPreprint
Che, T., Li, Y., Zhang, R., Hjelm, R. D., Li, W., Song, Y., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.07983 · Cited by 168 · see citing works
Homophily, Structure, and Content Augmented Network Representation Learning
Zhang, D., Yin, J., Xingquan Zhu, A., & Zhang, C. (2016) · Proceedings · doi:10.1109/icdm.2016.0072 · Cited by 112 · see citing works
Adversarial Generation of Natural LanguagePreprint
Rajeswar, S., Subramanian, S., Dutil, F., Pal, C., & Courville, A. C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10929 · Cited by 98 · see citing works
Adversarial Network EmbeddingPreprint
Dai, Q., Li, Q., Tang, J. G., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.07838 · Cited by 68 · see citing works
Adversarially Regularized Autoencoders for Generating Discrete Structures.Preprint
Zhao, J., Kim, Y., Zhang, K., Rush, A. M., & LeCun, Y. (2017) · arXiv (Cornell University) · Cited by 54 · see citing works
Embedding with Autoencoder Regularization
Yu, W., Zeng, G., Luo, P., Zhuang, F., He, Q., & Shi, Z. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40994-3_14 · Cited by 50 · see citing works
Self-citationReducing the maximum degree of a graph by deleting edges.
Borg, P., & Fenech, K. (2019) · OAR@UM (University of Malta) · Cited by 3 · see citing works
Reducing the maximum degree of a graph by deleting vertices
Borg, P., & Fenech, K. (2017) · OAR@UM (University of Malta) · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein genera- tive adversarial networks. In ICML. 214-223.
- Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolu- tional neural networks on graphs with fast localized spectral filtering. In NIPS. 3844-3852.
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In NIPS. 2672-2680.
- Leonardo F.R. Ribeiro, Pedro H.P. Saverese, and Daniel R. Figueiredo. 2017. Struc2Vec: Learning Node Representations from Structural Identity. In KDD. ACM, 385-394.
- Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural deep network embed- ding. In KDD. ACM, 1225-1234.
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A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000 · see citing works
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. (2020) · UTS ePRESS (University of Technology Sydney) · Cited by 3,174 · see citing works
Deep Learning on Graphs: A Survey
Zhang, Z., Cui, P., & Zhu, W. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2981333 · Cited by 1,729 · see citing works
Heterogeneous Graph Neural Network
Zhang, C., Song, D., Huang, C., Swami, A., & Chawla, N. V. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330961 · Cited by 1,591 · see citing works
Self-citationLearning Graph Embedding With Adversarial Training Methods
Pan, S., Hu, R., Fung, S.-F., Long, G., Jiang, J. B., & Zhang, C. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2932096 · Cited by 363 · see citing works
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators
Abadal, S., Jain, A., Guirado, R., López-Alonso, J., & Alarcón, E. (2021) · ACM Computing Surveys · doi:10.1145/3477141 · Cited by 293 · see citing works
Adversarial Learning on Heterogeneous Information Networks
Hu, B., Fang, Y., & Shi, C. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330970 · Cited by 238 · see citing works
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Feng, F., He, X., Tang, J., & Chua, T. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2957786 · Cited by 243 · see citing works
Graph Neural Network: A Comprehensive Review on Non-Euclidean Space
Asif, N. A., Sarker, Y., Chakrabortty, R. K., Ryan, M. J., Ahamed, M. H., Saha, D. K., Badal, F. R., Das, S. K., Ali, M. F., Moyeen, S. I., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3071274 · Cited by 230 · see citing works
Withnall, M., Lindelöf, E., Engkvist, O., & Chen, H. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-019-0407-y · Cited by 232 · see citing works
Self-citationGraph representation learning in bioinformatics: trends, methods and applications
Yi, H.-C., You, Z., Huang, D.-S., & Kwoh, C. K. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab340 · Cited by 224 · see citing works
Graph Neural Networks: Taxonomy, Advances, and Trends
Zhou, Y. P., Zheng, H., Huang, X., Hao, S., Li, D., & Zhao, J. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3495161 · Cited by 170 · see citing works
Survey on graph embeddings and their applications to machine learning problems on graphs
Makarov, I., Kiselev, D., Nikitinsky, N., & Šubelj, L. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.357 · Cited by 127 · see citing works
Generative Adversarial Networks for Spatio-temporal Data: A Survey
Gao, N., Xue, H., Shao, W., Zhao, S., Qin, K. K., Prabowo, A., Rahaman, M. S., & Salim, F. D. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3474838 · Cited by 127 · see citing works
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Wu, L., Qishan, Z., Chen, C., Guo, K., & Wang, D. (2020) · IEEE Access · doi:10.1109/access.2020.2996001 · Cited by 103 · see citing works
Advanced Deep Learning Models for 6G: Overview, Opportunities, and Challenges
Jiao, L., Shao, Y., Sun, L., Liu, F., Yang, S., Ma, W., Li, L., Liu, X., Hou, B., Zhang, X., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3418900 · Cited by 111 · see citing works
Adversarial Training Methods for Network Embedding
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Introduction to Graph Neural Networks
Liu, Z., & Zhou, J. (2020) · Synthesis lectures on artificial intelligence and machine learning · doi:10.1007/978-3-031-01587-8 · Cited by 129 · see citing works
Zhang, C., Swami, A., & Chawla, N. V. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291001 · Cited by 89 · see citing works
Query performance prediction for concurrent queries using graph embedding
Zhou, X., Sun, J., Li, G., & Feng, J. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3397230.3397238 · Cited by 85 · see citing works
Homogeneous network embedding for massive graphs via reweighted personalized PageRank
Yang, R., Shi, J. Y., Xiao, X., Yang, Y., & Bhowmick, S. S. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3377369.3377376 · Cited by 83 · see citing works
Deep Graph Learning for Anomalous Citation Detection
Liu, J., Xia, F., Xu, F., Ren, J. F., & Liu, H. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3145092 · Cited by 71 · see citing works
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph
Liu, L., Zhou, T., Long, G., Jiang, J., Yao, L., & Zhang, C. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/418 · Cited by 65 · see citing works
AI meets physics: a comprehensive survey
Jiao, L., Song, X., You, C., Liu, X., Li, L., Chen, P., Tang, X., Feng, Z., Liu, F., Guo, Y., et al. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10874-4 · Cited by 72 · see citing works
Graph Convolutional Networks: Algorithms, Applications and Open Challenges
Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04648-4_7 · Cited by 66 · see citing works
Adversarial Substructured Representation Learning for Mobile User Profiling
Wang, P., Fu, Y., Xiong, H., & Li, X. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330869 · Cited by 66 · see citing works
Familial Clustering for Weakly-Labeled Android Malware Using Hybrid Representation Learning
Zhang, Y., Sui, Y., Pan, S., Zheng, Z., Ning, B., Tsang, I. W.-H., & Zhou, W. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2947861 · Cited by 63 · see citing works
A Survey on the Recent Advances of Deep Community Detection
Souravlas, S. I., Anastasiadou, S., & Katsavounis, S. (2021) · Applied Sciences · doi:10.3390/app11167179 · Cited by 60 · see citing works
A Comparative Study for Unsupervised Network Representation Learning
Khosla, M., Setty, V., & Anand, A. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2951398 · Cited by 53 · see citing works
REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs
Pei, S., Lu, Y., Yu, G., & Zhang, X. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403268 · Cited by 50 · see citing works
Superpixel-Based Attention Graph Neural Network for Semantic Segmentation in Aerial Images
Diao, Q., Dai, Y., Zhang, C., Wu, Y. X., Feng, X., & Pan, F. (2022) · Remote Sensing · doi:10.3390/rs14020305 · Cited by 49 · see citing works
Adversarial Directed Graph Embedding
Zhu, S., Li, J., Peng, H. M., Wang, S., & He, L. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16605 · Cited by 48 · see citing works
Distribution-Induced Bidirectional Generative Adversarial Network for Graph Representation Learning
Zheng, S., Zhu, Z., Zhang, X., Liu, Z., Cheng, J., & Zhao, Y. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00725 · Cited by 45 · see citing works
Large-Scale Talent Flow Embedding for Company Competitive Analysis
Zhang, L., Xu, T., Zhu, H., Qin, C., Meng, Q., Xiong, H., & Chen, E. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380299 · Cited by 45 · see citing works
Unsupervised Graph Embedding via Adaptive Graph Learning
Zhang, R., Zhang, Y., Lu, C., & Li, X. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3202158 · Cited by 43 · see citing works
Graph Contrastive Learning with Generative Adversarial Network
Wu, C., Wang, C., Xu, J., Liu, Z., Zheng, K., Wang, X., Song, Y., & Gai, K. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599370 · Cited by 38 · see citing works
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Gkarmpounis, G., Vranis, C., Vretos, N., & Daras, P. (2024) · IEEE Access · doi:10.1109/access.2024.3456913 · Cited by 40 · see citing works
Introduction to Graph Neural Networks
Liu, Z., & Zhou, J. (2020) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00980ed1v01y202001aim045 · Cited by 37 · see citing works
Neighborhood Attention Networks With Adversarial Learning for Link Prediction
Wang, Z., Lei, Y., & Li, W. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015896 · Cited by 36 · see citing works
Unsupervised Adversarially Robust Representation Learning on Graphs
Xu, J., Yang, Y., Chen, J., Jiang, X., Wang, C., Lu, J., & Sun, Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i4.20349 · Cited by 35 · see citing works
A survey on graph embedding techniques for biomedical data: Methods and applications
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Deep Adversarial Completion for Sparse Heterogeneous Information Network Embedding
Zhao, K., Bai, T., Wu, B., Wang, B., Zhang, Y., Yang, Y., & Nie, J. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380134 · Cited by 34 · see citing works
Yang, C., Zhang, J., & Han, J. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00081 · Cited by 31 · see citing works
Network Representation Learning: From Traditional Feature Learning to Deep Learning
Sun, K., Wang, L., Xu, B., Zhao, W., Teng, S. W., & Xia, F. (2020) · IEEE Access · doi:10.1109/access.2020.3037118 · Cited by 30 · see citing works
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Jin, D., Wang, R., Wang, T., He, D., Ding, W., Huang, Y., Wang, L., & Pedrycz, W. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3166539 · Cited by 26 · see citing works
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Machine Learning with Shallow Neural Networks
Aggarwal, C. C. (2018) · Springer eBooks · doi:10.1007/978-3-319-94463-0_2 · Cited by 26 · see citing works
Self-citationLearning Robust Representations with Graph Denoising Policy Network
Wang, L., Yu, W., Wang, W. W., Cheng, W. D., Zhang, W., Hongyuan Zha, B., He, X., & Chen, H. (2019) · Proceedings · doi:10.1109/icdm.2019.00177 · Cited by 27 · see citing works
Self-citationEnhancing Unsupervised Anomaly Detection With Score-Guided Network
Huang, Z., Zhang, B., Hu, G., Li, L., Xu, Y., & Jin, Y. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3281501 · Cited by 25 · see citing works
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Multi-level graph contrastive learning
Shao, P., & Tao, J. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127101 · Cited by 27 · see citing works
CANE: community-aware network embedding via adversarial training
Wang, J., Cao, J., Li, W., & Wang, S. (2020) · Knowledge and Information Systems · doi:10.1007/s10115-020-01521-9 · Cited by 24 · see citing works
Learning graph attention-aware knowledge graph embedding
Li, C., Peng, X., Niu, Y., Zhang, S., Peng, H. M., Zhou, C. D., & Li, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.139 · Cited by 24 · see citing works
Adversarial Mutual Information Learning for Network Embedding
He, D., Zhai, L., Li, Z., Jin, D., Yang, L., Huang, Y., & Yu, P. S. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/459 · Cited by 23 · see citing works
Robust graph convolutional networks with directional graph adversarial training
Hu, W., Chen, C., Chang, Y., Zheng, Z., & Du, Y. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02272-y · Cited by 22 · see citing works
Adversarial Mobility Learning for Human Trajectory Classification
Gao, Q., Zhang, F., Yao, F., Li, A., Mei, L., & Zhou, F. (2020) · IEEE Access · doi:10.1109/access.2020.2968935 · Cited by 22 · see citing works
Zheng, C. Y., Zhang, Q., Long, G., Zhang, C., Young, S. D., & Wang, W. (2020) · IEEE Access · doi:10.1109/access.2020.2991683 · Cited by 22 · see citing works
Self-citationFeng, X., Zhao, Q., & Li, Y. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116260 · Cited by 21 · see citing works
LiSSA: Localized Stochastic Sensitive Autoencoders
Wang, T., Ng, W. W. Y., Pelillo, M., & Kwong, S. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2923756 · Cited by 21 · see citing works
Feature-Based Fusion Adversarial Recurrent Neural Networks for Text Sentiment Classification
Ma, Y., Fan, H., & Zhao, C. (2019) · IEEE Access · doi:10.1109/access.2019.2940506 · Cited by 20 · see citing works
X-GGM: Graph Generative Modeling for Out-of-distribution Generalization in Visual Question Answering
Jiang, J., Liu, Z., Liu, Y., Nan, Z., & Zheng, N. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475350 · Cited by 18 · see citing works
A Robust and Generalized Framework for Adversarial Graph Embedding
Li, J., Fu, X., Zhu, S., Peng, H. M., Wang, S., Sun, Q., Yu, P. S., & He, L. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3235944 · Cited by 18 · see citing works
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He, D., Wang, T., Zhai, L., Jin, D., Yang, L., Huang, Y., Feng, Z., & Yu, P. S. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3103193 · Cited by 18 · see citing works
Li, D., Li, D., & Lian, G. (2022) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3555809 · Cited by 17 · see citing works
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning
Bonner, S., Brennan, J., Kureshi, I., Theodoropoulos, G. K., McGough, A. S., & Obara, B. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622636 · Cited by 17 · see citing works
TPNE: Topology preserving network embedding
Xie, Y., Gong, M., Qin, A. K., Tang, Z., & Fan, X. (2019) · Information Sciences · doi:10.1016/j.ins.2019.07.035 · Cited by 17 · see citing works
TriATNE: Tripartite Adversarial Training for Network Embeddings
Liu, Q., Long, C., Zhang, J., Xu, M., & Lv, P. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3061771 · Cited by 17 · see citing works
Shi, Y., Quan, P., Xiao, Y., Lei, M., & Niu, L. (2022) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2022.3164474 · Cited by 16 · see citing works
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Zheng, C. Y., Zhang, Q., Young, S. D., & Wang, W. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412134 · Cited by 16 · see citing works
Self-citationEmbedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
Kotitsas, S., Pappas, D., Androutsopoulos, I., McDonald, R., & Apidianaki, M. (2019) · BioNLP Workshop and Shared Task (BioNLP) · doi:10.18653/v1/w19-5032 · Cited by 15 · see citing works
Said, A., Janjua, M. U., Hassan, S., Muzammal, Z., Saleem, T., Thaipisutikul, T., Tuarob, S., & Nawaz, R. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.815 · Cited by 14 · see citing works
Hybrid graph convolutional and deep convolutional networks for enhanced pavement crack detection
Song, Q., & Tian, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110227 · Cited by 14 · see citing works
Node Pair Information Preserving Network Embedding Based on Adversarial Networks
Wang, C., Shi, W. L., Huang, L., Lin, K.-Y., Huang, D., & Yu, P. S. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3035066 · Cited by 14 · see citing works
Neighbor-Anchoring Adversarial Graph Neural Networks
Liu, Z., Fang, Y., Liu, Y., & Zheng, V. W. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3087970 · Cited by 14 · see citing works
Clustering Noisy Trajectories via Robust Deep Attention Auto-Encoders
Zhang, R., Xie, P., Jiang, H., Xiao, Z. Q., Wang, C., & Liu, L. (2019) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2019.00-73 · Cited by 14 · see citing works
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Gao, Y., Shi, B., Dong, B., Wang, Y., Mi, L., & Zheng, Q. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3090075 · Cited by 14 · see citing works
Bipartite Graph Neural Networks for Efficient Node Representation LearningPreprint
He, C., Xie, T., Rong, Y., Huang, W., Li, Y., Huang, J., Ren, X., & Shahabi, C. (2019) · arXiv (Cornell University) · Cited by 14 · see citing works
Training Robust Graph Neural Networks with Topology Adaptive Edge DroppingPreprint
Gao, Z., Bhattacharya, S., Zhang, L., Blum, R. S., Ribeiro, A., & Sadler, B. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02892 · Cited by 14 · see citing works
Qing, L., Li, L., Wang, Y., Cheng, Y., & Peng, Y. (2021) · Remote Sensing · doi:10.3390/rs13112038 · Cited by 13 · see citing works
Power Attributed Graph Embedding and Clustering
Labiod, L., & Nadif, M. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183273 · Cited by 13 · see citing works
GAN‐based deep neural networks for graph representation learning
Zhao, M. J., & Zhang, Y. (2022) · Engineering Reports · doi:10.1002/eng2.12517 · Cited by 13 · see citing works
Self-Attentive Attributed Network Embedding Through Adversarial Learning
Yu, W., Cheng, W. D., Aggarwal, C. C., Zong, B., Chen, H., & Wang, W. (2019) · Proceedings · doi:10.1109/icdm.2019.00086 · Cited by 13 · see citing works
Self-citationGraph neural networks to model and optimize the operation of Water Distribution Networks: A review
Vittori, G., Falkouskaya, Y., Jimenez-Gutierrez, D. M., Cattai, T., & Chatzigiannakis, I. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100880 · Cited by 12 · see citing works
Noori, A., Balafar, M. A., Bouyer, A., & Salmani, K. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01178-6 · Cited by 12 · see citing works
Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks
Lai, Y., Lin, X., Yang, R., & Wang, H. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671765 · Cited by 12 · see citing works
Adversarial Training Methods for Network EmbeddingPreprint
Dai, Q., Shen, X. X., Zhang, L., Li, Q., & Wang, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.11514 · Cited by 12 · see citing works
Maghdid, S. S., Rashid, T. A., & Askar, S. K. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00797-w · Cited by 13 · see citing works
Xiao, J., Dai, Q., Xie, X., Lam, J., & Kwok, K. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110456 · Cited by 11 · see citing works
Graph embedding via multi-scale graph representations
Xie, Y., Chen, C., Gong, M., Li, D., & Qin, A. K. (2021) · Information Sciences · doi:10.1016/j.ins.2021.07.026 · Cited by 11 · see citing works
GT-SimNet: Improving code automatic summarization via multi-modal similarity networks
Gao, X., Jiang, X., Wu, Q., Wang, X., Lyu, C., & Lyu, L. (2022) · Journal of Systems and Software · doi:10.1016/j.jss.2022.111495 · Cited by 11 · see citing works
Learning Node Representations from Noisy Graph Structures
Wang, J., Li, Z., Long, Q., Zhang, W., Song, G., & Shi, C. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00169 · Cited by 11 · see citing works
Dadsetan, S., Pichler, D., Wilson, D., Hovakimyan, N., & Hobbs, J. A. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00330 · Cited by 11 · see citing works
Reciprocal GAN Through Characteristic Functions (RCF-GAN)
Li, S., Yu, Z., Xiang, M., & Mandic, D. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3157444 · Cited by 11 · see citing works
Hierarchical Graph Neural Network Based on Semi-Implicit Variational Inference
Su, H., Li, Z., Zhu, X.-B., Yang, L., Gribova, V. V., Filaretov, V. F., Cohn, A. G., & Huang, D.-S. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3193398 · Cited by 10 · see citing works
Wu, P., Zheng, C., & Pan, L. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3076766 · Cited by 10 · see citing works
Self-citationEnsemble of multimodal deep learning autoencoder for infant cry and pain detection
Kristian, Y., Simogiarto, N., Sampurna, M. T. A., Hanindito, E., & Visuddho, V. (2023) · F1000Research · doi:10.12688/f1000research.73108.2 · Cited by 10 · see citing works
Rich heterogeneous information preserving network representation learning
Yu, B., Hu, J., Xie, Y., Zhang, C. B. C., & Tang, Z. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107564 · Cited by 10 · see citing works
Regularizing transformers with deep probabilistic layers
Aguilera, A. C., Olmos, P. M., Artés-Rodrı́guez, A., & Pérez‐Cruz, F. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.01.032 · Cited by 11 · see citing works
- Authors
- Yu, W., Zheng, C., Cheng, W., Aggarwal, C. C., Song, D., Zong, B., Chen, H., & Wang, W.
- Affiliations
- University of California, Los Angeles (US)
- NEC Laboratories America, Princeton, NJ, USA
- IBM Research AI, Yorktown Height, NY, USA
- Venue
- Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2018-07-19
- Pages
- 2663-2671
- OpenAlex
- W2808987817
- Cited by
- 188
- References
- 40
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 15 · 2023: 19 · 2022: 30 · 2021: 42 · 2020: 38 · 2019: 26 · 2018: 4
- FWCI
- 11.23
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Funders
- National Institutes of Health (U01HG008488, R01GM115833, U54GM114833)
- National Science Foundation (IIS-1313606)
- Army Research Laboratory (W911NF-09-2-0053)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Graph Theory and Algorithms
- Keywords
- graph embedding, network representation learning, adversarial regularization, autoencoder, generative adversarial training, local structure preservation, global reconstruction, vertex representation, link prediction, network inference, multi-label classification
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