TY - GEN
T1 - Multiple social role embedding
AU - Xu, Linchuan
AU - Wei, Xiaokai
AU - Cao, Jiannong
AU - Yu, Philip S.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Network embedding has been increasingly employed in networked data mining applications as it is effective to learn node embeddings that encode the network structure. Existing network models usually learn a single embedding for each node. In practice, a person may interact with others in different roles, such as interacting with schoolmates as a student, and with colleagues as an employee. Obviously, different roles exhibit different characteristics or features. Hence, only learning a single embedding responsible for all roles is not appropriate. In this paper, we thus introduce a concept of multiple social role (MSR) into social network embedding for the first time. The MSR models multiple roles people play in society, such as student and employee. To make the embedding more versatile, we thus propose a multiple social role embedding (MSRE) model to preserve both the network structure and social roles. Empirical evaluation on various real-world social networks demonstrates advantages of the proposed MSRE over the state-of-the-art embedding models in link prediction and multi-label classification.
AB - Network embedding has been increasingly employed in networked data mining applications as it is effective to learn node embeddings that encode the network structure. Existing network models usually learn a single embedding for each node. In practice, a person may interact with others in different roles, such as interacting with schoolmates as a student, and with colleagues as an employee. Obviously, different roles exhibit different characteristics or features. Hence, only learning a single embedding responsible for all roles is not appropriate. In this paper, we thus introduce a concept of multiple social role (MSR) into social network embedding for the first time. The MSR models multiple roles people play in society, such as student and employee. To make the embedding more versatile, we thus propose a multiple social role embedding (MSRE) model to preserve both the network structure and social roles. Empirical evaluation on various real-world social networks demonstrates advantages of the proposed MSRE over the state-of-the-art embedding models in link prediction and multi-label classification.
KW - Data mining
KW - Network embedding
KW - Social networks
UR - http://www.scopus.com/inward/record.url?scp=85046262255&partnerID=8YFLogxK
U2 - 10.1109/DSAA.2017.23
DO - 10.1109/DSAA.2017.23
M3 - Conference article published in proceeding or book
AN - SCOPUS:85046262255
T3 - Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
SP - 581
EP - 589
BT - Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Data Science and Advanced Analytics, DSAA 2017
Y2 - 19 October 2017 through 21 October 2017
ER -