Effective Attributed Network Embedding with Information Behavior Extraction

Abstract Network embedding has shown its effectiveness in many tasks such as link prediction, node classification, and community detection. Most attributed network embedding methods consider topological features and attributed features to obtain a node embedding, but ignore its implicit information behavior features, including information inquiry, interaction, and sharing. This can potentially lead to ineffective performance for downstream applications. In this paper, we propose a novel network embedding framework named information behavior extraction ( IBE ), that incorporates nodes' topological features, attributed features, and information behavior features into a joint embedding framework. To design IBE , we use an existing embedding method (e.g., SDNE, CANE, or CENE) to extract a node's topological features and attributed features into a basic vector. Then, we propose a topic-sensitive network embedding ( TNE ) model to extract node information behavior features and eventually generate information behavior feature vectors. In our TNE model, we propose an importance score rating algorithm ( ISR ), which considers both effects of the topic-based community of a node and its interaction with adjacent nodes to capture a node information behavior features. Eventually, we concatenate a node information behavior feature vector with its basic vector to get its ultimate joint embedding vector. Extensive experiments demonstrate that our method achieves significant and consistent improvements, compared to several state-of-the-art embedding methods on link prediction.