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Robust cross-network node classification via constrained graph mutual information
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posted on 2023-02-14, 03:02 authored by S Yang, Borui Cai, T Cai, Xiangyu Song, J Jiang, B Li, Jianxin LiJianxin LiThe 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.
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Journal
Knowledge-Based SystemsVolume
257Article number
ARTN 109852Publisher DOI
ISSN
0950-7051eISSN
1872-7409Language
EnglishPublication classification
C1 Refereed article in a scholarly journalPublisher
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