A General Framework for Content-enhanced Network Representation Learning

This paper investigates the problem of network embedding, which aims at learning low-dimensional vector representation of nodes in networks. Most existing network embedding methods rely solely on the network structure, i.e., the linkage relationships between nodes, but ignore the rich content information associated with it, which is common in real world networks and beneficial to describing the characteristics of a node. In this paper, we propose content-enhanced network embedding (CENE), which is capable of jointly leveraging the network structure and the content information. Our approach integrates text modeling and structure modeling in a general framework by treating the content information as a special kind of node. Experiments on several real world networks with application to node classification show that our models outperform all existing network embedding methods, demonstrating the merits of content information and joint learning. Introduction Network embedding, which aims at learning lowdimensional vector representations of a network, has attracted increasing interest in recent years. It has been shown highly effective in many important tasks in network analysis involving predictions over nodes and edges, such as node classification (Tsoumakas and Katakis 2006; Sen et al. 2008), recommendation (Tu, Liu, and Sun 2014; Yu et al. 2014) and link prediction (Liben-Nowell and Kleinberg 2007). Various approaches have been proposed toward this goal, typically including Deepwalk (Perozzi, Al-Rfou, and Skiena 2014), LINE (Tang et al. 2015), GraRep (Cao, Lu, and Xu 2015), and node2vec (Grover and Leskovec 2016). These models have been proven effective in several real world networks. Most of the previous approaches utilize information only from the network structure, i.e., the linkage relationships between nodes, while paying scant attention to the content of each node, which is common in real-world networks. In a typical social network with users as vertices, the user-generated contents (e.g., texts, images) will serve as rich extra information which should be important for node representation and beneficial to downstream applications. Figure 1 shows an example network from Quora, a community question answering website. Users in Quora can follow each other, creating directed connections in the network. How does the shape