Abstract
This paper aims to investigate how the geographical footprints of users correlate to their social ties. While conventional wisdom told us that the more frequently two users co-locate in geography, the higher probability they are friends, we find that in real geo-social data, Gowalla and Meetup, almost all of the user pairs with friendships had never met geographically. In this sense, can we discover social ties among users purely using their geographical footprints even if they never met? To study this question, we develop a two-stage feature engineering framework. The first stage is to characterize the direct linkages between users through their spatial co-locations while the second is to capture the indirect linkages between them via a co-location graph. Experiments conducted on Gowalla check-in data and Meetup meeting events exhibit not only the superiority of our feature model, but also validate the predictability (with 70% accuracy) of detecting social ties solely from user footprints.
| Original language | English |
|---|---|
| Title of host publication | CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management |
| Publisher | Association for Computing Machinery |
| Pages | 1839-1842 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781450337946 |
| DOIs | |
| Publication status | Published - 2015 Oct 17 |
| Event | 24th ACM International Conference on Information and Knowledge Management, CIKM 2015 - Melbourne, Australia Duration: 2015 Oct 19 → 2015 Oct 23 |
Publication series
| Name | International Conference on Information and Knowledge Management, Proceedings |
|---|---|
| Volume | 19-23-Oct-2015 |
Other
| Other | 24th ACM International Conference on Information and Knowledge Management, CIKM 2015 |
|---|---|
| Country | Australia |
| City | Melbourne |
| Period | 15-10-19 → 15-10-23 |
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All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Business, Management and Accounting(all)
Cite this
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Where you go reveals who you know : Analyzing social ties from millions of footprints. / Hsieh, Hsun Ping; Yan, Rui; Li, Cheng Te.
CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, 2015. p. 1839-1842 (International Conference on Information and Knowledge Management, Proceedings; Vol. 19-23-Oct-2015).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Where you go reveals who you know
T2 - Analyzing social ties from millions of footprints
AU - Hsieh, Hsun Ping
AU - Yan, Rui
AU - Li, Cheng Te
PY - 2015/10/17
Y1 - 2015/10/17
N2 - This paper aims to investigate how the geographical footprints of users correlate to their social ties. While conventional wisdom told us that the more frequently two users co-locate in geography, the higher probability they are friends, we find that in real geo-social data, Gowalla and Meetup, almost all of the user pairs with friendships had never met geographically. In this sense, can we discover social ties among users purely using their geographical footprints even if they never met? To study this question, we develop a two-stage feature engineering framework. The first stage is to characterize the direct linkages between users through their spatial co-locations while the second is to capture the indirect linkages between them via a co-location graph. Experiments conducted on Gowalla check-in data and Meetup meeting events exhibit not only the superiority of our feature model, but also validate the predictability (with 70% accuracy) of detecting social ties solely from user footprints.
AB - This paper aims to investigate how the geographical footprints of users correlate to their social ties. While conventional wisdom told us that the more frequently two users co-locate in geography, the higher probability they are friends, we find that in real geo-social data, Gowalla and Meetup, almost all of the user pairs with friendships had never met geographically. In this sense, can we discover social ties among users purely using their geographical footprints even if they never met? To study this question, we develop a two-stage feature engineering framework. The first stage is to characterize the direct linkages between users through their spatial co-locations while the second is to capture the indirect linkages between them via a co-location graph. Experiments conducted on Gowalla check-in data and Meetup meeting events exhibit not only the superiority of our feature model, but also validate the predictability (with 70% accuracy) of detecting social ties solely from user footprints.
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UR - http://www.scopus.com/inward/citedby.url?scp=84958280866&partnerID=8YFLogxK
U2 - 10.1145/2806416.2806653
DO - 10.1145/2806416.2806653
M3 - Conference contribution
AN - SCOPUS:84958280866
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1839
EP - 1842
BT - CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
ER -