Abstract: Attributed network embedding aims to learn the low-dimensional representation of nodes for a given attributed network. Nodes with similar topology and attributes are close to each other in the embedding space. The attention mechanism can effectively learn the relative importance of nodes and their neighbors in the network, and aggregate the node representations based on the neighbor importance. According to this, a node embedding method that incorporates a two-layer attention mechanism in attributed network is proposed, which can effectively capture attributed network embedding. This method first captures direct neighbors from the topology and indirect neighbors based on attribute relationship, and effectively considers the relative importance of node neighbors in this process. Specifically, the direct neighbor and indirect neighbor of the node are first captured, and then the node-level attention mechanism is designed to aggregate the direct neighbor representation and the indirect neighbor representation respectively. Finally the semantic-level attention is designed to merge the two embedded representations to obtain the final embedding. Experiments on both real-world datasets and synthetic datasets verify the effectiveness of the proposed method.

Key words: node-level attention, semantic-level attention, attributed network, node embedding