TY - GEN
T1 - Attributed signed network embedding
AU - Wang, Suhang
AU - Aggarwal, Charu
AU - Tang, Jiliang
AU - Liu, Huan
N1 - Funding Information:
Œis material is based upon work supported by, or in part by, the National Science Foundation (NSF) under the grant #1614576 and Oce of Naval Research (ONR) under the grant N00014-16-1-2257.
Funding Information:
This material is based upon work supported by, or in part by, the National Science Foundation (NSF) under the grant #1614576 and Office of Naval Research (ONR) under the grant N00014-16-1-2257.
Publisher Copyright:
© 2017 ACM.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - e major task of network embedding is to learn low-dimensional vector representations of social-network nodes. It facilitates many analytical tasks such as link prediction and node clustering and thus has a.racted increasing attention. The majority of existing embedding algorithms are designed for unsigned social networks. However, many social media networks have both positive and negative links, for which unsigned algorithms have li.le utility. Recent findings in signed network analysis suggest that negative links have distinct properties and added value over positive links.This brings about both challenges and opportunities for signed network embedding. In addition, user attributes, which encode properties and interests of users, provide complementary information to network structures and have the potential to improve signed network embedding.Therefore, in this paper, we study the novel problem of signed social network embedding with attributes. We propose a novel framework SNEA, which exploits the network structure and user attributes simultaneously for network representation learning. Experimental results on link prediction and node clustering with real-world datasets demonstrate the effectiveness of SNEA.
AB - e major task of network embedding is to learn low-dimensional vector representations of social-network nodes. It facilitates many analytical tasks such as link prediction and node clustering and thus has a.racted increasing attention. The majority of existing embedding algorithms are designed for unsigned social networks. However, many social media networks have both positive and negative links, for which unsigned algorithms have li.le utility. Recent findings in signed network analysis suggest that negative links have distinct properties and added value over positive links.This brings about both challenges and opportunities for signed network embedding. In addition, user attributes, which encode properties and interests of users, provide complementary information to network structures and have the potential to improve signed network embedding.Therefore, in this paper, we study the novel problem of signed social network embedding with attributes. We propose a novel framework SNEA, which exploits the network structure and user attributes simultaneously for network representation learning. Experimental results on link prediction and node clustering with real-world datasets demonstrate the effectiveness of SNEA.
KW - Network embedding
KW - Node attributes
KW - Signed social networks
UR - http://www.scopus.com/inward/record.url?scp=85037328915&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85037328915&partnerID=8YFLogxK
U2 - 10.1145/3132847.3132905
DO - 10.1145/3132847.3132905
M3 - Conference contribution
AN - SCOPUS:85037328915
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 137
EP - 146
BT - CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Y2 - 6 November 2017 through 10 November 2017
ER -