MHCNC: A Novel Framework for Multi-Source Heterogeneous Cross-Network Node Classification

Abstract

In this study, we consider the issue of multi-source heterogeneous cross-network node classification, which employs the plentiful labeled information from multiple source networks to assist in classifying unlabeled nodes in a target network. Traditional single-source cross-network node classification methods mainly focus on the scenario where the source and target networks share the same feature spaces. The current multi-source transfer learning methods are generally unable to model the network's structural information. Thus, both of them cannot be applied to the problem of multi-source heterogeneous cross-network node classification. In this study, we introduce a multi-source heterogeneous cross-network node classification (MHCNC) framework, which integrates multi-source heterogeneous transfer learning with graph convolutional neural network to learn network-invariant and label-discriminative node representations. In MHCNC, we first devise the heterogeneous feature transformation that transforms the multiple source feature spaces onto the target network to obtain new feature representations to mitigate the distribution discrepancy between networks. In addition, we incorporate an inductive learning-based convolutional neural network to classify the unlabeled nodes in a target network. Moreover, we propose a novel model fusion strategy based on the classification weight of each model. Comprehensive experiments conducted on real-world social networks verify that our algorithms outperform the most advanced methods in terms of classification accuracy.