- Title
- A3Graph : adversarial attributed autoencoder for graph representation learning
- Creator
- Hou, Mingliang; Wang, Lei; Liu, Jiaying; Kong, Xiangjie; Xia, Feng
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176827
- Identifier
- vital:15188
- Identifier
-
https://doi.org/10.1145/3412841.3442042
- Identifier
- ISBN:9781450381048 (ISBN)
- Abstract
- Recent years have witnessed a proliferation of graph representation techniques in social network analysis. Graph representation aims to map nodes in the graph into low-dimensional vector space while preserving as much information as possible. However, most existing methods ignore the robustness of learned latent vectors, which leads to inferior representation results due to sparse and noisy data in graphs. In this paper, we propose a novel framework, named A3Graph, which aims to improve the robustness and stability of graph representations. Specifically, we first construct an aggregation matrix by the combining positive point-wise mutual information matrix with the attribute matrix. Then, we enforce the autoencoder to reconstruct the aggregation matrix instead of the input attribute matrix. The enhancement autoencoder can incorporate structural and attributed information in a joint learning way to improve the noise-resilient during the learning process. Furthermore, an adversarial learning component is leveraged in our framework to impose a prior distribution on learned representations has been demonstrated as an effective mechanism in improving the robustness and stability in representation learning. Experimental studies on real-world datasets have demonstrated the effectiveness of the proposed A3Graph. © 2021 ACM.
- Publisher
- Association for Computing Machinery
- Relation
- 36th Annual ACM Symposium on Applied Computing, SAC 2021 p. 1697-1704
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2021 Association for Computing Machinery
- Rights
- Open Access
- Subject
- Adversarial autoencoder; Attributed information; Graph learning; Social network
- Full Text
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