Paper Info
Title
Quantitative Analysis of Community Detection Methods for Longitudinal Mobile Data
Abstract
Mobile phones are now equipped with increasingly large number of built-in sensors that can be utilized to collect long-term socio-temporal data of social interactions. Moreover, the data from different built-in sensors can be combined to predict social interactions. In this paper, we perform quantitative analysis of 6 community detection algorithms to uncover the community structure from the mobile data. We use Bluetooth, WLAN, GPS, and contact data for analysis, where each modality is modelled as an undirected weighted graph. We evaluate community detection algorithms across 6 inter-modality pairs, and use well know partition evaluation features to measure clustering similarity between the pairs. We compare the performance of different methods based on the delivered partitions, and analyse the graphs at different times to find out the community stability.
Year
DOI
Venue
2013
10.1109/SOCIETY.2013.17
SOCIETY '13 Proceedings of the 2013 International Conference on Social Intelligence and Technology
Keywords
Field
DocType
community detection methods,social interaction,mobile data,community stability,longitudinal mobile data,contact data,different method,quantitative analysis,community structure,different time,community detection algorithm,different built-in sensor,long-term socio-temporal data,bluetooth,graph theory,sensors,global positioning system,indexes,gps,mobile computing,wlan
Mobile computing,Social science,Graph theory,Data mining,Graph,Community structure,Sociology,Global Positioning System,Cluster analysis,Mobile broadband,Bluetooth
Conference
ISBN
Citations 
PageRank 
978-1-4799-0045-9
2
0.36
References 
Authors
4
2
Name
Order
Citations
PageRank
Syed Agha Muhammad171.12
K Van Laerhoven21083185.94