Level: Master 
Responsible person: Dr. Jian Hong Lin
Keywords: Centrality Ranking, Temporal Networks, Dynamic Models 

Identifying key nodes in temporal networks is crucial for understanding complex dynamic processes such as information dissemination, epidemic outbreaks, and communication patterns[1-3]. This project introduces innovative temporal centrality measures that integrate both the time-evolving nature of network topology and the dynamics of the underlying processes[4]. By evaluating these measures across diverse systems—ranging from social networks to biological and technological infrastructures—we aim to enhance the accuracy of predicting influential nodes, optimize network interventions, and improve the resilience of dynamic systems. The outcomes of this research have the potential to inform strategies for controlling epidemics, optimizing communication networks, and developing more adaptive and robust infrastructures. 

Project Goals: 

This project will focus on the following objectives: 

1. Literature Review: 
   Conduct a detailed review of existing research on temporal networks and centrality measures, particularly focusing on the impact of network dynamics on node influence. The review should identify key gaps and opportunities for innovation. 

2. Development of Temporal Centrality Measures: 
   Develop and refine temporal centrality measures that consider both the evolving nature of networks and the dynamics of the processes occurring within them. These measures should be applicable across various types of networks. 

3. Application and Evaluation: 
   Apply the developed centrality measures to different real-world networks, including social, biological, and technological systems. Evaluate the performance of these measures in accurately identifying influential nodes and improving network resilience. 

4. Optimization of Network Interventions: 
   Explore how the insights gained from the centrality measures can be used to optimize interventions within networks, such as controlling the spread of epidemics or enhancing communication efficiency. 

5. Final Report and Presentation: 
   Compile the research findings into a comprehensive report, detailing the methodologies, results, and implications of the study. Prepare a presentation to communicate the key outcomes and potential applications of the research. 

Student Learning Outcomes: 

• Gain in-depth knowledge of temporal networks and the dynamic processes within them. 

• Develop skills in designing and implementing centrality measures tailored to time-dependent network structures. 

• Learn how to analyze and optimize interventions in complex networks to improve their performance and resilience. 

• Enhance abilities in conducting research, critically evaluating results, and effectively communicating findings. 

References: 

1. Holme, P., & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. This paper provides a comprehensive review of temporal networks and introduces methods for analyzing time-dependent structures. 

2. Valdano, E., Ferreri, L., Poletto, C., & Colizza, V. (2015). Analytical Computation of the Epidemic Threshold on Temporal Networks. Physical Review X, 5(2), 021005. This work explores the impact of temporal dynamics on epidemic thresholds and node importance. 

3. Kim, H., & Anderson, R. (2012). Temporal Node Centrality in Complex Networks. Physical Review E, 85(2), 026107. This paper introduces and evaluates temporal centrality measures for dynamic networks. 

4. Liu, J. G., Lin, J. H., Guo, Q., & Zhou, T. (2016). Locating influential nodes via dynamics-sensitive centrality. Scientific reports, 6(1), 21380.