Semi-Supervised Embedding of Attributed Multiplex Networks

Complex information can be represented as networks (graphs) characterized by a large number of nodes, multiple types of nodes, and multiple types of relationships between them, i.e. multiplex networks. Additionally, these networks are enriched with different types of node features. We propose a Semi-supervised Embedding approach for Attributed Multiplex Networks (SSAMN), to jointly embed nodes, node attributes, and node labels of multiplex networks in a low dimensional space. Network embedding techniques have garnered research attention for real-world applications. However, most existing techniques solely focus on learning the node embeddings, and only a few learn class label embeddings. Our method assumes that we have different classes of nodes and that we know the class label of some, very few nodes for every class. Guided by this type of supervision, SSAMN learns a low-dimensional representation incorporating all information in a large labeled multiplex network. SSAMN integrates techniques from Spectral Embedding and Homogeneity Analysis to improve the embedding of nodes, node attributes, and node labels. Our experiments demonstrate that we only need very few labels per class in order to have a final embedding that preservers the information of the graph. To evaluate the performance of SSAMN, we run experiments on four real-world datasets. The results show that our approach outperforms state-of-the-art methods for downstream tasks such as semi-supervised node classification and node clustering.