Domain-adaptive Message Passing Graph Neural Network

• URL: http://arxiv.org/abs/2308.16470v2
• Date: Tue, 17 Oct 2023 04:57:23 GMT
• Title: Domain-adaptive Message Passing Graph Neural Network
• Authors: Xiao Shen, Shirui Pan, Kup-Sze Choi, Xi Zhou
• Abstract summary: Cross-network node classification (CNNC) aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels. We propose a domain-adaptive message passing graph neural network (DM-GNN), which integrates graph neural network (GNN) with conditional adversarial domain adaptation.
• Score: 67.35534058138387
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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