Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1660-1663 |
| Number of pages | 4 |
| ISBN (Print) | 9781467384926 |
| DOIs | |
| State | Published - Jan 29 2016 |
| Event | 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 - Atlantic City, United States Duration: Nov 14 2015 → Nov 17 2015 |
Other
| Other | 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 |
|---|---|
| Country | United States |
| City | Atlantic City |
| Period | 11/14/15 → 11/17/15 |
Fingerprint
Keywords
- cross-site
- Graph propagation
- semantic matching
- virtual social network
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications
Cite this
Cross-Site Virtual Social Network Construction. / Xie, Chenhao; Yang, Deqing; He, Jingrui; Xiao, Yanghua.
Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1660-1663 7395882.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Cross-Site Virtual Social Network Construction
AU - Xie, Chenhao
AU - Yang, Deqing
AU - He, Jingrui
AU - Xiao, Yanghua
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 - cross-site
KW - Graph propagation
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
SN - 9781467384926
SP - 1660
EP - 1663
BT - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -