How Long Will She Call Me? Distribution, Social Theory and Duration Prediction
Abstract
Call duration analysis is a key issue for understanding underlying patterns of (mobile) phone users. In this paper, we study to which extent the duration of a call between users can be predicted in a dynamic mobile network. We have collected a mobile phone call data from a mobile operating company, which results in a network of 272,345 users and 3.9 million call records during two months. We first examine the dynamic distribution properties of the mobile network including periodicity and demographics. Then we study several important social theories in the call network including strong/weak ties, link homophily, opinion leader and social balance. The study reveals several interesting phenomena such as people with strong ties tend to make shorter calls and young females tend to make long calls, in particular in the evening. Finally, we present a time-dependent factor graph model to model and infer the call duration between users, by incorporating our observations in the distribution analysis and the social theory analysis. Experiments show that the presented model can achieve much better predictive performance compared to several baseline methods. Our study offers evidences for social theories and also unveils several different patterns in the call network from online social networks.
Cite
Text
Dong et al. "How Long Will She Call Me? Distribution, Social Theory and Duration Prediction." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40991-2_2Markdown
[Dong et al. "How Long Will She Call Me? Distribution, Social Theory and Duration Prediction." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/dong2013ecmlpkdd-long/) doi:10.1007/978-3-642-40991-2_2BibTeX
@inproceedings{dong2013ecmlpkdd-long,
title = {{How Long Will She Call Me? Distribution, Social Theory and Duration Prediction}},
author = {Dong, Yuxiao and Tang, Jie and Lou, Tiancheng and Wu, Bin and Chawla, Nitesh V.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {16-31},
doi = {10.1007/978-3-642-40991-2_2},
url = {https://mlanthology.org/ecmlpkdd/2013/dong2013ecmlpkdd-long/}
}