Multi-modal Mining and Modeling of Big Mobile Networks Based on Users Behavior and Interest

TL;DR

This paper demonstrates that modeling user interests and behaviors in big mobile networks significantly improves traffic prediction accuracy over traditional generic models, using a novel graph-based and co-clustering approach.

Contribution

The paper introduces a new interest-based modeling approach for big mobile networks, leveraging graph-based KS-test and co-clustering techniques to enhance accuracy.

Findings

Interest-based models reduce KS distance by a factor of 5.

User interests based on domains and locations impact network traffic.

Novel graph and co-clustering methods improve modeling precision.

Abstract

Usage of mobile wireless Internet has grown very fast in recent years. This radical change in availability of Internet has led to communication of big amount of data over mobile networks and consequently new challenges and opportunities for modeling of mobile Internet characteristics. While the traditional approach toward network modeling suggests finding a generic traffic model for the whole network, in this paper, we show that this approach does not capture all the dynamics of big mobile networks and does not provide enough accuracy. Our case study based on a big dataset including billions of netflow records collected from a campus-wide wireless mobile network shows that user interests acquired based on accessed domains and visited locations as well as user behavioral groups have a significant impact on traffic characteristics of big mobile networks. For this purpose, we utilize a novel graph-based approach based on KS-test as well as a novel co-clustering technique. Our study shows that interest-based modeling of big mobile networks can significantly improve the accuracy and reduce the KS distance by factor of 5 comparing to the generic approach.