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Domain-adaptive Message Passing Graph Neural Network
- Source :
- Neural Networks, vol. 164, pp. 439-454, 2023
- Publication Year :
- 2023
-
Abstract
- Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently. To address CNNC, we propose a domain-adaptive message passing graph neural network (DM-GNN), which integrates graph neural network (GNN) with conditional adversarial domain adaptation. DM-GNN is capable of learning informative representations for node classification that are also transferrable across networks. Firstly, a GNN encoder is constructed by dual feature extractors to separate ego-embedding learning from neighbor-embedding learning so as to jointly capture commonality and discrimination between connected nodes. Secondly, a label propagation node classifier is proposed to refine each node's label prediction by combining its own prediction and its neighbors' prediction. In addition, a label-aware propagation scheme is devised for the labeled source network to promote intra-class propagation while avoiding inter-class propagation, thus yielding label-discriminative source embeddings. Thirdly, conditional adversarial domain adaptation is performed to take the neighborhood-refined class-label information into account during adversarial domain adaptation, so that the class-conditional distributions across networks can be better matched. Comparisons with eleven state-of-the-art methods demonstrate the effectiveness of the proposed DM-GNN.<br />Comment: This version rectifies the numerical inaccuracies of Table 3 and 4 in the printed version (https://doi.org/10.1016/j.neunet.2023.04.038). See our corrigendum at https://doi.org/10.1016/j.neunet.2023.09.026
- Subjects :
- Computer Science - Machine Learning
Subjects
Details
- Database :
- arXiv
- Journal :
- Neural Networks, vol. 164, pp. 439-454, 2023
- Publication Type :
- Report
- Accession number :
- edsarx.2308.16470
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1016/j.neunet.2023.04.038