Robust cross-network node classification via constrained graph mutual information
Article
Yang, Shuiqiao, Cai, Borui, Cai, Taotao, Song, Xiangyu, Jiang, Jiaojiao, Li, Bing and Li, Jianxin. 2022. "Robust cross-network node classification via constrained graph mutual information." Knowledge-Based Systems. 257. https://doi.org/10.1016/j.knosys.2022.109852
| Article Title | Robust cross-network node classification via constrained graph mutual information |
|---|---|
| ERA Journal ID | 18062 |
| Article Category | Article |
| Authors | Yang, Shuiqiao, Cai, Borui, Cai, Taotao, Song, Xiangyu, Jiang, Jiaojiao, Li, Bing and Li, Jianxin |
| Journal Title | Knowledge-Based Systems |
| Journal Citation | 257 |
| Article Number | 109852 |
| Number of Pages | 10 |
| Year | 2022 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 0950-7051 |
| 1872-7409 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.knosys.2022.109852 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0950705122009455 |
| Abstract | The recent methods for cross-network node classification mainly exploit graph neural networks (GNNs) as feature extractor to learn expressive graph representations across the source and target graphs. However, GNNs are vulnerable to noisy factors, such as adversarial attacks or perturbations on the node features or graph structure, which can cause a significant negative impact on their learning performance. To this end, we propose a robust graph domain adaptive learning framework RGDAL which exploits an information-theoretic principle to filter the noisy factors for cross-network node classification. Specifically, RGDAL utilizes graph convolutional network (GCN) with constrained graph mutual information and an adversarial learning component to learn noise-resistant and domain-invariant graph representations. To overcome the difficulties of estimating the mutual information for the non independent and identically distributed (non-i.i.d.) graph structured data, we design a dynamic neighborhood sampling strategy that can discretize the graph and incorporate the graph structural information for mutual information estimation. Experimental results on two real-world graph datasets demonstrate that RGDAL shows better robustness for cross-network node classification compared with the SOTA graph adaptive learning methods. |
| Keywords | Graph domain adaptive learning; Node classification; Graph neural networks; Mutual information |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 4602. Artificial intelligence |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | University of New South Wales |
| Deakin University | |
| Macquarie University | |
| Agency for Science Technology And Research, Singapore |
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