Node-Edge Bilateral Attributed Network Embedding

This paper addresses attributed network embedding which maps the structural information and multi-modal attribute data into a latent space. Most existing network embedding algorithms concentrate on either node-oriented modeling or edge-oriented modeling, resulting in unilaterally capturing information from nodes or edges. However, there is no effective method to bilaterally extract node attributes cooperated with edge attributes, which delineates the outline and detail of social network. To this end, we propose a novel Node-Edge Bilateral Attributed Network Embedding method named NEBANE. Regarding each edge as a specific node, we construct a pioneering node-edge-node triangular structure for bilateral information modeling on both nodes and edges. Furthermore, we envisage a pairwise loss which maximizes the likelihood of connected node pairs and of connected node-edge pairs to measure the node-node and node-edge similarity. Empirically, experiments on two real-world datasets, including link prediction and node classification, are conducted in this paper. Our method achieves substantial performance gains compared with state-of-the-art baselines (e.g., 4.21%–13.65% lift by AUC scores for link prediction).