Data reconstruction on networks

Nowadays, we are surrounded by a large volume of data. They are an integral part of our everyday activates such as social network interactions, banking data, health monitoring, marketing and so forth. A large portion of this data is generated from a network; for instance Facebook data, fMRI measurements in brain networks, and transportation data. The complexity of the network on which these data reside renders direct signal processing a challenging task. Graph signal processing is a novel research area that aims at exploiting this network structure to efficiently analyze the data at hand.

A key task in network data is to reconstruct missing values from few measurements. Consider as an example a survey performed on a social network. The reconstruction task consists of finding the survey outcome at users that did not participate in it by exploiting the outcomes of their friends of friends. An efficient solution of this task would require few people to take part in surveys and save both time and memory in large networks.

Assignment

Data reconstruction over networks has been currently investigated with promising results. These preliminary findings can be exploited to formulate the problem for the task at hand, this can be changing networks of time-varying signals.

In this project, your task will be to formulate an accurate model for the considered data reconstruction task. You further need to perform a theoretical analysis of the reconstructed scheme. You will work with both simulated and real data and you are requested to compare the performance with other state-ofthe- art approaches.

Requirements

The project requires a self-motivated student who has a strong linear algebra and statistical signal processing background, and is interested in theoretical and practical aspects of anomaly detection on networks. Furthermore, you are required to respect deadlines in delivering the results. You should have advanced coding skill in one computer language, including Matlab, Python, or R.

Contact

prof.dr.ir. Geert Leus

Signal Processing Systems Group

Department of Microelectronics

Last modified: 2018-03-21