Classical and Quantum Approaches to Network Data Analysis

Analyzing real-world systems through network models is essential for understanding the complex interactions between elements that form nonlinear, adaptive and self-organizing patterns and structures. The primary goals include understanding the structure and dynamics of networks, identifying key players or components, detecting hidden patterns or communities, predicting future interactions, and optimizing networks for desired outcomes. Network data analysis provides powerful tools and insights across various fields, facilitating better decision making, forecasting, and optimization in complex interconnected systems.

In the realm of quantum technologies, quantum techniques for network data analysis present novel methods for tackling complex problems that are hard with classical approaches. These methods leverage the principles of quantum mechanics to address computationally intensive or even intractable problems, offering potential speedups and new capabilities in analyzing and processing network data. This represents a cutting-edge approach to studying and understanding complex networks.

In this Research Topic, we aim to gather recent advancements and promising future perspectives on mathematical and statistical models, analysis, and the application of network data analysis as a tool for real-world challenges. Our focus is on classical, quantum, and hybrid classical-quantum approaches to developing network methods for analyzing complex data.

We are particularly interested in the following topics:

● Graphical Models
● Diffusion Phenomena on Network Systems
● Quantum Dynamics in Complex Networks
● Network Representation of Quantum Systems
● Random Walks on Graphs
● Graph Neural Networks
● Financial Networks
● Sampling from Complex Networks
● Graph Partitioning and Decomposition
● Clustering and Community Detection
● Higher-order Interactions in Complex Networks
● Multilayer Network Models
● Multi-agent Systems