Attributed Network Embedding via Edge Information Enhancing for Wireless Communication Network

The continuous development of wireless communication networks makes the interaction between users become frequent, and the analysis of network users has become a hot topic. Attributed network embedding combines user relationship and user attributes in the network to learn high-quality node representations. However, Existing attributed network embedding method ignore the edge information and the information enhancement of node attributes on the edge, so ultimately high-quality node representation cannot be obtained. In order to make full use of node attributes to enhance network edge information, we propose an Attributed Network Embedding via Edge Information Enhancing(ANEE) method. Our method uses random walk based on network structure and node attributes to obtain sequences. After that, In node sequence learning and edge sequence learning these two sequence learning, we adopt Self-Attention mechanism in multiple latent spaces to learn multiple latent correlation information in the sequence, so as to obtain better node representations. In the node sequence learning, the position information in the sequence is added as a constraint, and the node representation with a reasonable position is learned. In edge sequence learning, the attribute relation of adjacent nodes is added to edge sequence relation as additional information to enhance edge information. The performance of model proposed in this paper has been verified by experiments on several real data sets. The result shows that the model is superior to the state-of-the-art algorithm in link prediction task.