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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
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
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
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 20,507
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
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
(2016) · Cited by 11,679
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
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
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
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 7,021
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
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 6,684
(2017) · International Conference on Machine Learning · Cited by 6,382
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
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,977
Optimal Transport: Old and New
Villani, C. (2013) · Grundlehren der mathematischen Wissenschaften · doi:10.1007/978-3-540-71050-9 · Cited by 3,838
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 2,921
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
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 2,422
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 2,295
Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015) · arXiv · doi:10.48550/arxiv.1511.05644 · Cited by 1,579
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 1,344
Envelope Theorems for Arbitrary Choice Sets
Milgrom, P., & Segal, I. (2002) · Econometrica · doi:10.1111/1468-0262.00296 · Cited by 1,295
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
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 1,077
Clustering in weighted networks
Opsahl, T., & Panzarasa, P. (2009) · Social Networks · doi:10.1016/j.socnet.2009.02.002 · Cited by 1,037
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
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 704
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
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
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
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 455
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 393
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
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 259
Towards time-aware link prediction in evolving social networks
Tylenda, T., Angelova, R., & Bedathur, S. (2009) · Social Network Mining and Analysis · doi:10.1145/1731011.1731020 · Cited by 216
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
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
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
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
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
Self-citationReducing the maximum degree of a graph by deleting edges.
Borg, P., & Fenech, K. (2019) · OAR@UM (University of Malta) · Cited by 3
Reducing the maximum degree of a graph by deleting vertices
Borg, P., & Fenech, K. (2017) · OAR@UM (University of Malta) · Cited by 3
Flagged first, then by citations
Research works printed but not linked: 2
- Michaël Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering NIPS. 3844--3852. Michaël Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering 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. 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.
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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
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
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 1,822
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
Self-citationLearning Graph Embedding With Adversarial Training Methods
Pan, S., Hu, R., Fung, S.-F., Long, G., Jiang, J. B., & Chengqi Zhang, B. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2932096 · Cited by 366
Metadata
- 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 › Computational Theory and Mathematics › 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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