The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Adversarial Network Embedding
Open accessAbstract
Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization. Existing methods can effectively encode different structural properties into the representations, such as neighborhood connectivity patterns, global structural role similarities and other high-order proximities. However, except for objectives to capture network structural properties, most of them suffer from lack of additional constraints for enhancing the robustness of representations. In this paper, we aim to exploit the strengths of generative adversarial networks in capturing latent features, and investigate its contribution in learning stable and robust graph representations. Specifically, we propose an Adversarial Network Embedding (ANE) framework, which leverages the adversarial learning principle to regularize the representation learning. It consists of two components, i.e., a structure preserving component and an adversarial learning component. The former component aims to capture network structural properties, while the latter contributes to learning robust representations by matching the posterior distribution of the latent representations to given priors. As shown by the empirical results, our method is competitive with or superior to state-of-the-art approaches on benchmark network embedding tasks.
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Cites
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
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
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
Collective Classification in Network Data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., & Eliassi‐Rad, T. (2008) · AI Magazine · doi:10.1609/aimag.v29i3.2157 · Cited by 3,293
ArnetMiner: extraction and mining of academic social networks
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Self-citationEnergy-based Generative Adversarial Network
Zhao, J., Mathieu, M., & LeCun, Y. (2016) · arXiv · doi:10.48550/arxiv.1609.03126 · Cited by 912 · cited 1×
“Firstly, some works managed to integrate some structures into the framework to do inference, i.e., projecting sample in data space back into the space of latent features, such as BiGAN (Donahue, Kr¨ahenb¨uhl, and Darrell 2017), ALI (Dumoulin et al. 2017) and EBGAN (Zhao, Mathieu, and LeCun 2016).”
BackgroundChen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6
Cited by
Adversarially Regularized Graph Autoencoder for Graph Embedding
Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/362 · Cited by 929
Network Representation Learning: A Survey
Zhang, D., Yin, J., Zhu, X., & Zhang, C. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2850013 · Cited by 714
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
Shen, X., Pan, S., Liu, W., Ong, Y.-S., & Sun, Q.-S. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/493 · Cited by 57
Metadata
- Authors
- Dai, Q., Li, Q., Tang, J., & Wang, D.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-26
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2962904108
- Cited by
- 210
- References
- 8
- FWCI
- 15.81
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Dai, Q., Li, Q., Tang, J., & Wang, D. (2018). Adversarial Network Embedding. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v32i1.11865
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