Abstract
We propose a density-based model to detect communities of users in geo-social networks that are both socially and spatially cohesive. After formally defining the model and the geo-social distance measure it relies on, we present an algorithm that correctly identifies the underlying communities. We assess the effectiveness of our method using novel quantitative measures on the quality of the discovered communities. We also perform a visual evaluation of the discovered communities, using both real and synthetic datasets. Our results show that the proposed model produces geo-social communities with strong social and spatial cohesiveness, which can not be captured by existing graph or spatial clustering methods.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD 2019 |
| Publisher | Association for Computing Machinery |
| Pages | 110-119 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781450362801 |
| DOIs | |
| Publication status | Published - 19 Aug 2019 |
| Event | 16th International Symposium on Spatial and Temporal Databases, SSTD 2019 - Vienna, Austria Duration: 19 Aug 2019 → 21 Aug 2019 |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|
Conference
| Conference | 16th International Symposium on Spatial and Temporal Databases, SSTD 2019 |
|---|---|
| Country | Austria |
| City | Vienna |
| Period | 19/8/19 → 21/8/19 |
Keywords
- Community detection
- Geo-social networks
- Graph clustering
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Software
Cite this
Density-based community detection in geo-social networks. / Yao, Kai; Papadias, Dimitris; Bakiras, Spiridon.
Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD 2019. Association for Computing Machinery, 2019. p. 110-119 (ACM International Conference Proceeding Series).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Density-based community detection in geo-social networks
AU - Yao, Kai
AU - Papadias, Dimitris
AU - Bakiras, Spiridon
PY - 2019/8/19
Y1 - 2019/8/19
N2 - We propose a density-based model to detect communities of users in geo-social networks that are both socially and spatially cohesive. After formally defining the model and the geo-social distance measure it relies on, we present an algorithm that correctly identifies the underlying communities. We assess the effectiveness of our method using novel quantitative measures on the quality of the discovered communities. We also perform a visual evaluation of the discovered communities, using both real and synthetic datasets. Our results show that the proposed model produces geo-social communities with strong social and spatial cohesiveness, which can not be captured by existing graph or spatial clustering methods.
AB - We propose a density-based model to detect communities of users in geo-social networks that are both socially and spatially cohesive. After formally defining the model and the geo-social distance measure it relies on, we present an algorithm that correctly identifies the underlying communities. We assess the effectiveness of our method using novel quantitative measures on the quality of the discovered communities. We also perform a visual evaluation of the discovered communities, using both real and synthetic datasets. Our results show that the proposed model produces geo-social communities with strong social and spatial cohesiveness, which can not be captured by existing graph or spatial clustering methods.
KW - Community detection
KW - Geo-social networks
KW - Graph clustering
UR - http://www.scopus.com/inward/record.url?scp=85071613961&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071613961&partnerID=8YFLogxK
U2 - 10.1145/3340964.3340966
DO - 10.1145/3340964.3340966
M3 - Conference contribution
AN - SCOPUS:85071613961
T3 - ACM International Conference Proceeding Series
SP - 110
EP - 119
BT - Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD 2019
PB - Association for Computing Machinery
ER -