- Title
- Cross network representation matching with outliers
- Creator
- Hou, Mingliang; Ren, Jing; Febrinanto, Febrinanto; Shehzad, Ahsan; Xia, Feng
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/187892
- Identifier
- vital:17179
- Identifier
-
https://doi.org/10.1109/ICDMW53433.2021.00124
- Identifier
- ISBN:2375-9232 (ISSN); 9781665424271 (ISBN)
- Abstract
- Research has revealed the effectiveness of network representation techniques in handling diverse downstream machine learning tasks upon graph structured data. However, most network representation methods only seek to learn information in a single network, which fails to learn knowledge across different networks. Moreover, outliers in real-world networks pose great challenges to match distribution shift of learned embeddings. In this paper, we propose a novel joint learning framework, called CrossOSR, to learn network-invariant embeddings across different networks in the presence of outliers in the source network. To learn outlier-aware representations, a modified graph convolutional network (GCN) layer is designed to indicate the potential outliers. To learn more fine-grained information between different domains, a subdomain matching is adopted to align the shift distribution of learned vectors. To learn robust network representations, the learned indicator is utilized to smooth the noise effect from source domain to target domain. Extensive experimental results on three real-world datasets in the node classification task show that the proposed framework yields state-of-the-art cross network representation matching performance with outliers in the source network. © 2021 IEEE.
- Publisher
- IEEE Computer Society
- Relation
- 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, Virtual, online, 7-10 December 2021, IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December, p. 951-958
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 IEEE
- Rights
- Open Access
- Subject
- Graph data; Graph learning; Network representation; Outlier; Subdomain matching
- Full Text
- Reviewed
- Funder
- National Natural Science Foundation of China under Grant No. 61872054.
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