Paper Info
Title
A regularization framework for mobile social network analysis
Abstract
Mobile phone data provides rich dynamic information on human activities in social network analysis. In this paper, we represent data from two different modalities as a graph and functions defined on the vertex set of the graph. We propose a regularization framework for the joint utilization of these two modalities of data, which enables us to model evolution of social network information and efficiently classify relationships among mobile phone users. Simulations based on real world data demonstrate the potential application of our model in dynamic scenarios, and present competitive results to baseline methods for combining multimodal data in the learning and clustering communities.
Year
DOI
Venue
2011
10.1109/ICASSP.2011.5946750
ICASSP
Keywords
Field
DocType
regularization framework,social network information,model evolution,mobile phone data,regularization on graphs,mobile social network analysis,multimodal data,social networking (online),classification and clustering,mobile computing,mobile computer,bluetooth,mobile communication,data mining,clustering,classification,global positioning system,social network analysis,social network
Modalities,Mobile computing,Data mining,Social network,Mobile social network,Computer science,Social network analysis,Artificial intelligence,Mobile phone,Cluster analysis,Machine learning,Mobile telephony
Conference
ISSN
ISBN
Citations 
1520-6149 E-ISBN : 978-1-4577-0537-3
978-1-4577-0537-3
2
PageRank 
References 
Authors
0.41
5
4
Name
Order
Citations
PageRank
Xiaowen Dong124922.07
Pascal Frossard23015230.41
Pierre Vandergheynst33576208.25
Nikolai Nefedov412010.62