Leveraging Contact Pattern to Predict Future Contact Pattern in Mobile Networks


With advances in the Internet and mobile technology, and decreasing cost of mobile devices, large scale pervasive networks are now ubiquitous in solving many earlier service limitations. Here, the challenge lies in its underlying temporal graph. It introduces technical limitations in efficient routing, maximal coverage with minimal latency, data offloading, to effective dissemination over mobile networks or mobility induced dynamic networks. Efficient solution to these interrelated problems lies in the novel prediction strategies for most accurate future contacts (links or interactions), their future contact time etc. In contrast to the existing strategies that consider either network structure or regular pattern and periodic nature of contacts, we propose a novel stochastic Poisson process model (variants of cascaded non-homogeneous Poisson process) that employ multi-recurrent, dependent contact pattern as its basis. We predict number of contacts relative to a node and over all nodes in any future interval, future contact time over a user and a pair of users. Finally, we validate our model with a widely used empirical data set from mobile network, and compare our model with doubly recurrent and homogeneous Poisson process model to conclude the superiority of our prediction model.

Meeting Name

8th ACM International Workshop on Hot Topics in Planet-Scale mObile computing and online Social Networking, HotPOST '16 (2016: Jul. 5-8, Paderborn, Germany)


Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Forecasting; Mobile computing; Mobile devices; Poisson distribution; Social networking (online); Social sciences computing; Stochastic models; Stochastic systems; Wireless networks; Dynamic network; Homogeneous Poisson process; Mobile Technology; Network structures; Non homogeneous poisson process; Number of contacts; Pervasive networks; Technical limitations; Mobile telecommunication systems; Contact Prediction; Mobility Induced Dynamic Networks

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2016 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Jul 2016