Predicting human contacts in mobile social networks using supervised learning

Authors:
Kazem Jahanbakhsh;Valerie King;Gholamali C. Shoja
Affiliations:
University of Victoria, B. C., Canada;University of Victoria, B. C., Canada;University of Victoria, B. C., Canada
Venue:
Proceedings of the Fourth Annual Workshop on Simplifying Complex Networks for Practitioners
Year:
2012

Citing 8
Cited 1

Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
The link-prediction problem for social networks

Journal of the American Society for Information Science and Technology
How Small Labels Create Big Improvements

PERCOMW '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications Workshops
Predicting positive and negative links in online social networks

Proceedings of the 19th international conference on World wide web
Human Contact Prediction Using Contact Graph Inference

GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Human mobility, social ties, and link prediction

Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Jyotish: Constructive approach for context predictions of people movement from joint Wifi/Bluetooth trace

Pervasive and Mobile Computing
Predicting missing contacts in mobile social networks

WOWMOM '11 Proceedings of the 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks

Fast track article: Predicting missing contacts in mobile social networks

Pervasive and Mobile Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Having access to human contact traces has allowed researchers to study and understand how people contact each other in different social settings. However, most of the existing human contact traces are limited in the number of deployed Bluetooth sensors. In most experiments, there are two types of participants, the ordinary ones who carry cellphones and a specially selected group who additionally carry sensors. Although the contacts between any pair of participants are known when at least one of them carry a sensor, the contacts between any pair of participants are "hidden" when both of them carry their cellphones. In this paper, we employ two well-known supervised classifiers for predicting hidden contacts among participants who carry their cellphones. The performance results of our supervised classifiers show the applicability of using machine learning algorithms for contact prediction task. The results also show that a small subset of features such as number of common neighbors and total overlap time play essential roles in forming human contacts. Finally, we show that contacts of nodes with high centralities are more predictable than nodes with low centralities.