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 ID18062
Article CategoryArticle
AuthorsYang, Shuiqiao, Cai, Borui, Cai, Taotao, Song, Xiangyu, Jiang, Jiaojiao, Li, Bing and Li, Jianxin
Journal TitleKnowledge-Based Systems
Journal Citation257
Article Number109852
Number of Pages10
Year2022
PublisherElsevier
Place of PublicationNetherlands
ISSN0950-7051
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
AbstractThe 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.
KeywordsGraph domain adaptive learning; Node classification; Graph neural networks; Mutual information
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
Public NotesFiles associated with this item cannot be displayed due to copyright restrictions.
Byline AffiliationsUniversity of New South Wales
Deakin University
Macquarie University
Agency for Science Technology And Research, Singapore
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