Abstract
Social networking has provided powerful new ways to find people, organize groups, and share information. Recently, the potential functionalities of the ubiquitous infrastructure let users form a mobile social network (MSN) which is discriminative against the previous social networks based on the Internet. Since a mobile phone is used in a much wider range of situations and is carried by the user at all times, it easily collects personal information and can be customized to fit the user's preference. In this paper, we presented MSN mining model which estimates the social contexts like closeness and relationship from uncertain phone logs using a Bayesian network. The mining results were then used for recommending callees or representing the state of social relationships. We have implemented the phonebook application that displays the contexts as network or graph style, and have performed a subjectivity test. As a result, we have confirmed that the visualizing of the MSN is useful as an interface for social networking services.
| Original language | English |
|---|---|
| Title of host publication | Ubiquitous Intelligence and Computing - 6th International Conference, UIC 2009, Proceedings |
| Pages | 111-120 |
| Number of pages | 10 |
| DOIs | |
| Publication status | Published - 2009 Nov 9 |
| Event | 6th International Conference on Ubiquitous Intelligence and Computing, UIC 2009 - Brisbane, QLD, Australia Duration: 2009 Jul 7 → 2009 Jul 9 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 5585 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 6th International Conference on Ubiquitous Intelligence and Computing, UIC 2009 |
|---|---|
| Country | Australia |
| City | Brisbane, QLD |
| Period | 09/7/7 → 09/7/9 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Mining and visualizing mobile social network based on bayesian probabilistic model. / Min, Jun Ki; Jang, Su Hyung; Cho, Sung Bae.
Ubiquitous Intelligence and Computing - 6th International Conference, UIC 2009, Proceedings. 2009. p. 111-120 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5585 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Mining and visualizing mobile social network based on bayesian probabilistic model
AU - Min, Jun Ki
AU - Jang, Su Hyung
AU - Cho, Sung Bae
PY - 2009/11/9
Y1 - 2009/11/9
N2 - Social networking has provided powerful new ways to find people, organize groups, and share information. Recently, the potential functionalities of the ubiquitous infrastructure let users form a mobile social network (MSN) which is discriminative against the previous social networks based on the Internet. Since a mobile phone is used in a much wider range of situations and is carried by the user at all times, it easily collects personal information and can be customized to fit the user's preference. In this paper, we presented MSN mining model which estimates the social contexts like closeness and relationship from uncertain phone logs using a Bayesian network. The mining results were then used for recommending callees or representing the state of social relationships. We have implemented the phonebook application that displays the contexts as network or graph style, and have performed a subjectivity test. As a result, we have confirmed that the visualizing of the MSN is useful as an interface for social networking services.
AB - Social networking has provided powerful new ways to find people, organize groups, and share information. Recently, the potential functionalities of the ubiquitous infrastructure let users form a mobile social network (MSN) which is discriminative against the previous social networks based on the Internet. Since a mobile phone is used in a much wider range of situations and is carried by the user at all times, it easily collects personal information and can be customized to fit the user's preference. In this paper, we presented MSN mining model which estimates the social contexts like closeness and relationship from uncertain phone logs using a Bayesian network. The mining results were then used for recommending callees or representing the state of social relationships. We have implemented the phonebook application that displays the contexts as network or graph style, and have performed a subjectivity test. As a result, we have confirmed that the visualizing of the MSN is useful as an interface for social networking services.
UR - http://www.scopus.com/inward/record.url?scp=70350638592&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350638592&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-02830-4_10
DO - 10.1007/978-3-642-02830-4_10
M3 - Conference contribution
AN - SCOPUS:70350638592
SN - 3642028292
SN - 9783642028298
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 111
EP - 120
BT - Ubiquitous Intelligence and Computing - 6th International Conference, UIC 2009, Proceedings
ER -