Detecting Edge Changes in Networks via Graph Signal Processing

Complex networks, often modeled as graphs, arise in various technological domains, including social, financial, transportation, communication, and smart grid networks. In practical scenarios, network topology can change randomly over time due to factors like link failures, evolving social connections, roadblocks, or sensor malfunctions. Additionally, adversarial actions and rapid climate change increasingly disrupt critical infrastructure networks, such as energy, water, and transportation systems. Unaddressed disruptions can lead to system-wide failures, highlighting the urgent need for quick and robust identification methods.

Assignment

A key challenge in this context is the dynamic nature of network edges, where additions or deletions can significantly impact system behavior. Failing to account for these changes can lead to poorly controlled network dynamics. This research focuses on tracking topological changes by observing network states as the structure evolves randomly over time. Positioned at the intersection of graph signal processing, control theory, and network science, this work aims to develop theoretical and algorithmic tools to address these challenges effectively. Additionally, you will explore applications in practical domains such as brain data, financial networks, and sensor networks. The project will be jointly supervised by Geethu Joseph (g.joseph@tudelft.nl) and Elvin Isulfi (e.isufi-1@tudelft.nl) from the Multimedia Computing Group. 

Requirements

For this project, we are looking for a master's student in either electrical engineering or any related study. Furthermore, we are looking for a student who has a background in signal processing, basic statistical techniques, data analysis, and programming skills in Matlab, Python, and/or C/C++. Strong communication (written and verbal) skills in English are mandatory. 

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Last modified: 2025-02-14