Density-based community detection in geo-social networks

Kai Yao, Dimitris Papadias, Spiridon Bakiras

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

1 Citation (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD 2019
PublisherAssociation for Computing Machinery
Pages110-119
Number of pages10
ISBN (Electronic)9781450362801
DOIs
Publication statusPublished - 19 Aug 2019
Event16th International Symposium on Spatial and Temporal Databases, SSTD 2019 - Vienna, Austria
Duration: 19 Aug 201921 Aug 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference16th International Symposium on Spatial and Temporal Databases, SSTD 2019
CountryAustria
CityVienna
Period19/8/1921/8/19

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

Yao, K., Papadias, D., & Bakiras, S. (2019). Density-based community detection in geo-social networks. In Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD 2019 (pp. 110-119). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3340964.3340966