This master’s thesis, titled Predictability of complex networks by Xuetong Zhao, investigates the fundamental question of how predictable the behavior and evolution of complex networks are. Complex networks—such as social, biological, and technological systems—exhibit intricate structures and dynamic behaviors that challenge traditional analytical methods. The thesis is situated within the Department of Electrical Engineering, Computer Engineering, and Informatics at Cyprus University of Technology, and was completed under the supervision of Fragkiskos Papadopoulos in February 2024.
The work addresses the theoretical and practical aspects of network predictability, exploring the extent to which future states or structural changes in a network can be anticipated based on current information. The study leverages recent advances in network science, statistical mechanics, and computational modeling to analyze both synthetic and real-world network data.
Theoretical Framework: The thesis develops a rigorous framework for quantifying predictability in complex networks. This includes defining appropriate metrics and criteria for assessing how well future network configurations or dynamics can be forecasted from present data.
Methodological Advances: It introduces or adapts analytical and computational techniques—potentially including machine learning, statistical inference, and random walk models—to assess and improve predictability. The work may also compare the effectiveness of different approaches across various types of networks.
Empirical Evaluation: The research applies the proposed methods to a range of network datasets, demonstrating how predictability varies with network topology, size, and the nature of interactions. Results likely highlight which structural features (e.g., degree distribution, clustering, modularity) enhance or limit predictability.
Case Studies: By examining specific real-world networks (such as social, communication, or biological systems), the thesis illustrates the practical implications of its findings, showing how predictability insights can inform network design, intervention strategies, or risk assessment.
The thesis makes significant contributions to the field of network science by clarifying the limits and possibilities of predicting complex network behavior. Understanding predictability is crucial for a wide range of applications, including epidemic modeling, infrastructure resilience, information diffusion, and cybersecurity. By providing a systematic approach to measuring and enhancing predictability, the research offers valuable tools for scientists and engineers working with complex systems.
Moreover, the findings have broader implications for the design and management of networked systems. Improved predictability can lead to more effective control strategies, better resource allocation, and enhanced robustness against failures or attacks. The thesis thus serves as a bridge between theoretical insights and practical applications, advancing both the science and engineering of complex networks.