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 attribute features to obtain a node embedding but ignore its implicit information behavior features, including information inquiry, interaction, and sharing. These can potentially lead to ineffective performance for downstream applications. In this article, we propose a novel network embedding framework, named information behavior extraction (IBE), that incorporates nodes’ topological features, attribute features, and information behavior features within 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 attribute features into a basic vector. Then, we propose a topic-sensitive network embedding (TNE) model to extract a node’s information behavior features and eventually generate information behavior feature vectors. In our TNE model, we design 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 the node’s information behavior features. Eventually, we concatenate a node’s 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.