Abstract
We propose a Key Graph-based context sharing method in mobile environment. With the recent advancement of mobile sensors, a variety of mobile applications become vehicles for improving our lives. Context sharing system which shares the user behaviors, emotion, and location is one of the promising fields for the social network service. It is a difficult problem to determine whether a user will share the personal information or not. In typical social network models, users are grouped in communities, and nodes of the same community have strong social links between each other. However, some nodes also have social links outside their "home" community. They have social relationships with users of different groups. Most systems concentrate on generating internal "home" community regardless of outside social relation. In this paper, we classify the personal information into two types. First type is the information to be shared with "home" community only. Second type is the information to be shared with as many people as possible. We utilize Key Graph algorithm to select a home community for sharing the personal contexts. Key Graph extracts two types of people who have strong social relationships in a community and have social links with many different communities. In order to show the feasibility of the proposed method, we conduct experiments to extract the user communities from Bluetooth data and implement a real-time context sharing application.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 |
| Pages | 2002-2006 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2013 Dec 1 |
| Event | 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 - Beijing, China Duration: 2013 Aug 20 → 2013 Aug 23 |
Publication series
| Name | Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 |
|---|
Other
| Other | 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 13/8/20 → 13/8/23 |
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All Science Journal Classification (ASJC) codes
- Software
Cite this
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KeyGraph-based social network generation for mobile context sharing. / Lee, Myeong Chun; Lee, Young Seol; Cho, Sung Bae.
Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013. 2013. p. 2002-2006 6682385 (Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - KeyGraph-based social network generation for mobile context sharing
AU - Lee, Myeong Chun
AU - Lee, Young Seol
AU - Cho, Sung Bae
PY - 2013/12/1
Y1 - 2013/12/1
N2 - We propose a Key Graph-based context sharing method in mobile environment. With the recent advancement of mobile sensors, a variety of mobile applications become vehicles for improving our lives. Context sharing system which shares the user behaviors, emotion, and location is one of the promising fields for the social network service. It is a difficult problem to determine whether a user will share the personal information or not. In typical social network models, users are grouped in communities, and nodes of the same community have strong social links between each other. However, some nodes also have social links outside their "home" community. They have social relationships with users of different groups. Most systems concentrate on generating internal "home" community regardless of outside social relation. In this paper, we classify the personal information into two types. First type is the information to be shared with "home" community only. Second type is the information to be shared with as many people as possible. We utilize Key Graph algorithm to select a home community for sharing the personal contexts. Key Graph extracts two types of people who have strong social relationships in a community and have social links with many different communities. In order to show the feasibility of the proposed method, we conduct experiments to extract the user communities from Bluetooth data and implement a real-time context sharing application.
AB - We propose a Key Graph-based context sharing method in mobile environment. With the recent advancement of mobile sensors, a variety of mobile applications become vehicles for improving our lives. Context sharing system which shares the user behaviors, emotion, and location is one of the promising fields for the social network service. It is a difficult problem to determine whether a user will share the personal information or not. In typical social network models, users are grouped in communities, and nodes of the same community have strong social links between each other. However, some nodes also have social links outside their "home" community. They have social relationships with users of different groups. Most systems concentrate on generating internal "home" community regardless of outside social relation. In this paper, we classify the personal information into two types. First type is the information to be shared with "home" community only. Second type is the information to be shared with as many people as possible. We utilize Key Graph algorithm to select a home community for sharing the personal contexts. Key Graph extracts two types of people who have strong social relationships in a community and have social links with many different communities. In order to show the feasibility of the proposed method, we conduct experiments to extract the user communities from Bluetooth data and implement a real-time context sharing application.
UR - http://www.scopus.com/inward/record.url?scp=84893472327&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893472327&partnerID=8YFLogxK
U2 - 10.1109/GreenCom-iThings-CPSCom.2013.375
DO - 10.1109/GreenCom-iThings-CPSCom.2013.375
M3 - Conference contribution
AN - SCOPUS:84893472327
SN - 9780769550466
T3 - Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
SP - 2002
EP - 2006
BT - Proceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
ER -