The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Learning Graph Embedding With Adversarial Training Methods
Open accessAbstract
Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the reconstruction errors for graph data. They have mostly overlooked the embedding distribution of the latent codes, which unfortunately may lead to inferior representation in many cases. In this article, we present a novel adversarially regularized framework for graph embedding. By employing the graph convolutional network as an encoder, our framework embeds the topological information and node content into a vector representation, from which a graph decoder is further built to reconstruct the input graph. The adversarial training principle is applied to enforce our latent codes to match a prior Gaussian or uniform distribution. Based on this framework, we derive two variants of the adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, and adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively. We also exploit other potential variations of ARGA and ARVGA to get a deeper understanding of our designs. Experimental results that compared 12 algorithms for link prediction and 20 algorithms for graph clustering validate our solutions.
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Cites
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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
Self-citationnode2vec: Scalable Feature Learning for Networks
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Cited by
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
Self-citationAttributed Graph Clustering: A Deep Attentional Embedding Approach
Wang, C., Pan, S., Hu, R., Long, G., Jiang, J., & Zhang, C. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/509 · Cited by 573
Self-citationGraph-based deep learning for communication networks: A survey
Jiang, W. (2022) · Computer Communications · doi:10.1016/j.comcom.2021.12.015 · Cited by 284
Qian, S., Xue, D., Fang, Q., & Xu, C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3188547 · Cited by 68
Data-Agnostic Model Poisoning Against Federated Learning: A Graph Autoencoder Approach
Li, K., Zheng, J., Yuan, X., Ni, W., Akan, O. B., & Poor, H. V. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3362147 · Cited by 41
Metadata
- Authors
- Pan, S., Hu, R., Fung, S.-F., Long, G., Jiang, J., & Zhang, C.
- Venue
- IEEE Transactions on Cybernetics
- Published
- 2020-06
- Volume
- 50
- Issue
- 6
- Pages
- 2475-2487
- ISSN
- 2168-2267 (ISSN-L)
- 2168-2275
- OpenAlex
- W2972209102
- Cited by
- 363
- References
- 66
- FWCI
- 17.96
- Type
- journal article
- Language
- en
- Open access
- green
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