Attribute network joint embedding based on global attention

Abstract

The attribute network not only has a complex topology, but its nodes also contain rich attribute information. Attribute network embedding methods extract both network topology and node attribute information to learn low-dimensional embedding of large attribute networks, which are of great importance for network analysis. In this paper, we propose attribute network joint embedding based on global attention (GAJE). First, GAJE uses the structure information of the attribute network to obtain the structure embedding vectors of nodes. Second, we propose a global attention method to obtain the attribute embedding vectors of nodes. This method captures the relationships of different attributes within and between nodes through convolutional neural networks and attention mechanisms, respectively. Finally, GAJE concatenates the structure embedding vectors and the attribute embedding vectors to obtain the final joint embedding vectors which simultaneously reflects the network structure and attributes information. For link prediction and node classification, we compared GAJE with nine well-known network embedding methods in four real datasets. The experimental results show that the algorithm has good attribute network embedding effects. Our tensorflow implementation of the GAJE is available at https://github.com/Andrewsama/GAJE-master.