  Embedding network data into a low-dimensional vector space has shown
promising performance for many real-world applications, such as node
classification and entity retrieval. However, most existing methods focused
only on leveraging network structure. For social networks, besides the network
structure, there also exists rich information about social actors, such as user
profiles of friendship networks and textual content of citation networks. These
rich attribute information of social actors reveal the homophily effect,
exerting huge impacts on the formation of social networks. In this paper, we
explore the rich evidence source of attributes in social networks to improve
network embedding. We propose a generic Social Network Embedding framework
(SNE), which learns representations for social actors (i.e., nodes) by
preserving both the structural proximity and attribute proximity. While the
structural proximity captures the global network structure, the attribute
proximity accounts for the homophily effect. To justify our proposal, we
conduct extensive experiments on four real-world social networks. Compared to
the state-of-the-art network embedding approaches, SNE can learn more
informative representations, achieving substantial gains on the tasks of link
prediction and node classification. Specifically, SNE significantly outperforms
node2vec with an 8.2% relative improvement on the link prediction task, and a
12.7% gain on the node classification task.
