  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 net- works with application to node classification show that
our models outperform all existing network embedding methods, demonstrating the
merits of content information and joint learning.
