TY - GEN
T1 - Cross-Site Virtual Social Network Construction
AU - Xie, Chenhao
AU - Yang, Deqing
AU - He, Jingrui
AU - Xiao, Yanghua
N1 - Funding Information:
This demo was supported by NSFC (No.61472085, 61171132, 61033010), by National Key Basic Research Program of China under No.2015CB358800, by Basic research project of Shanghai science and technology innovation action plan under No.15JC1400900, and by Shanghai Science and Technology Development Funds (13dz2260200, 13511504300).
Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/29
Y1 - 2016/1/29
N2 - Given the plethora of social networking sites, it can be difficult for users to browse too many sites and discover social friends. For example, for a new diabetes patient, how can s/he find the users with similar symptoms on different dedicated sites and form supporting groups with them? Since different sites may use different vocabularies, this problem is challenging to match users across different sites. To address it, in this paper, we present a tool to demonstrate how to construct a virtual social network across multiple social networking sites. Specifically, it uses bipartite graphs to represent the relation ships between users and their posts' keywords in each site, it bridges the gap between different vocabularies of different sites based on their semantic relatedness through concept-based interpretations, and it uses an efficient propagation algorithm to obtain the similarity between users from different sites, which can be used to construct the cross-site virtual social network.
AB - Given the plethora of social networking sites, it can be difficult for users to browse too many sites and discover social friends. For example, for a new diabetes patient, how can s/he find the users with similar symptoms on different dedicated sites and form supporting groups with them? Since different sites may use different vocabularies, this problem is challenging to match users across different sites. To address it, in this paper, we present a tool to demonstrate how to construct a virtual social network across multiple social networking sites. Specifically, it uses bipartite graphs to represent the relation ships between users and their posts' keywords in each site, it bridges the gap between different vocabularies of different sites based on their semantic relatedness through concept-based interpretations, and it uses an efficient propagation algorithm to obtain the similarity between users from different sites, which can be used to construct the cross-site virtual social network.
KW - Graph propagation
KW - cross-site
KW - semantic matching
KW - virtual social network
UR - http://www.scopus.com/inward/record.url?scp=84964777703&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964777703&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2015.98
DO - 10.1109/ICDMW.2015.98
M3 - Conference contribution
AN - SCOPUS:84964777703
T3 - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
SP - 1660
EP - 1663
BT - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
A2 - Wu, Xindong
A2 - Tuzhilin, Alexander
A2 - Xiong, Hui
A2 - Dy, Jennifer G.
A2 - Aggarwal, Charu
A2 - Zhou, Zhi-Hua
A2 - Cui, Peng
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Y2 - 14 November 2015 through 17 November 2015
ER -
