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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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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
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
Wu, P., Zheng, C., & Pan, L. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3076766 · Cited by 10
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
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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
Regularizing transformers with deep probabilistic layers
Aguilera, A. C., Olmos, P. M., Artés-Rodríguez, A., & Pérez‐Cruz, F. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.01.032 · Cited by 13
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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