Friendship

World Wide Web

Engineering

Social psychology

Node (physics)

Theoretical computer science

Homophily

Economics

Data mining

Computer science

Social network (sociolinguistics)

Complex network

Embedding

Psychology

Task (project management)

Mathematics

Management

Social media

Interpersonal ties

Social network analysis

Data science

Artificial intelligence

Combinatorics

Structural engineering

Information retrieval

Evolving networks

Machine learning

IEEE Transactions on Knowledge and Data Engineering

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.

Attributed Social Network Embedding