Domain-adaptive Message Passing Graph Neural Network

TL;DR

This paper introduces DM-GNN, a domain-adaptive message passing graph neural network designed for cross-network node classification, effectively transferring knowledge between networks with different label distributions using adversarial domain adaptation.

Contribution

The paper proposes a novel GNN framework with dual feature extractors, label propagation, and class-conditional domain adaptation for improved cross-network node classification.

Findings

DM-GNN outperforms eleven state-of-the-art methods.

Effective transfer of node representations across networks.

Enhanced intra-class propagation and domain adaptation.

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.