KeyGraph-based social network generation for mobile context sharing

Myeong Chun Lee, Young Seol Lee, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings - 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
Pages2002-2006
Number of pages5
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 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 202013 Aug 23

Publication series

NameProceedings - 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

Other2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
CountryChina
CityBeijing
Period13/8/2013/8/23

Fingerprint

Bluetooth
Sensors
Experiments

All Science Journal Classification (ASJC) codes

  • Software

Cite this

Lee, M. C., Lee, Y. S., & Cho, S. B. (2013). KeyGraph-based social network generation for mobile context sharing. In 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 (pp. 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). https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.375
Lee, Myeong Chun ; Lee, Young Seol ; Cho, Sung Bae. / KeyGraph-based social network generation for mobile context sharing. 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. pp. 2002-2006 (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).
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Lee, MC, Lee, YS & Cho, SB 2013, KeyGraph-based social network generation for mobile context sharing. in 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., 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, pp. 2002-2006, 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, 13/8/20. https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.375

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 proceedingConference contribution

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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.

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M3 - Conference contribution

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Lee MC, Lee YS, Cho SB. KeyGraph-based social network generation for mobile context sharing. In 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). https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.375