Coupled Tensor Decomposition for User Clustering in Mobile Internet Traffic Interaction Pattern

Abstract

The rapid developments in mobile Internet are reshaping our lives and activities. Understanding the user behaviors and dynamics in such a large-scale network is essential for better system design, service provisioning, and network management. In this paper, we focus on the interaction pattern between mobile users and servers based on the traffic flow data. Real traffic flow data is collected from the public network of ISPs by high-performance network traffic monitors. Traffic flow-based heterogeneous information network (TF-HIN) is introduced to represent the traffic interaction pattern, and node correlation characteristics are mined from TF-HIN. Based on the empirical analysis of traffic interaction pattern, we propose the coupled flow tensor to represent the relations among the user, server and time, by incorporating correlations of user and server as auxiliary information. Two iterative algorithms, i.e., FTD and FTD-NFS, are proposed for coupled flow tensor decomposition and the latent factors are used for user clustering. We evaluate the proposed user clustering algorithms by using benchmark datasets and also analyze the user clustering results from real traffic flow dataset. The numerical experiments show that the use of coupled flow tensor with auxiliary information provides a novel and scalable user clustering method and improves the clustering accuracy.