High-order proximity and relation analysis for cross-network heterogeneous node classification

Cross-network node classification aims to leverage the labeled nodes from a source network to assist the learning in a target network. Existing approaches work mainly in homogeneous settings, i.e., the nodes of the source and target networks are characterized by the same features. However, in many practical applications, nodes from different networks usually have heterogeneous features. To handle this issue, in this paper, we study the cross-network node classification under heterogeneous settings, i.e., cross-network heterogeneous node classification. Specifically, we propose a new model called High-order Proximity and Relation Analysis, which studies the high-order proximity in each network and the high-order relation between nodes across the networks to obtain two kinds of features. Subsequently, these features are exploited to learn the final effective representations by introducing a feature matching mechanism and an adversarial domain adaptation. We perform extensive experiments on several real-world datasets and make comparisons with existing baseline methods. Experimental results demonstrate the effectiveness of the proposed model.