Manuscript Topics

A complex network is a graph with non-trivial topological features, representing a wide range of systems in nature and society, such as neural networks, genetic regulatory networks, multi-agents system, cyber-physical systems, social systems, and so on. Unlike simple, regular graphs, they are characterized by properties like a skewed degree distribution (often scale-free), high clustering, small average path lengths (the "small-world" phenomenon), and community structure. Examples are ubiquitous: the Internet, social networks, biological neural networks, and metabolic pathways all exhibit these characteristics.

The study of complex networks, revitalized by the discovery of small-world and scale-free models, is inherently interdisciplinary, drawing from graph theory, mathematics, physics, and computer science. It provides a powerful framework for understanding how local interactions give rise to global system behavior, resilience, and dynamics, such as information spreading or cascade failures. This makes network science crucial for analyzing and managing complex systems in our interconnected world.

Building upon this foundation, complex network theory not only offers crucial insights into the understanding of natural and social systems but also provides new perspectives for the structural optimization and performance enhancement of artificial intelligence systems. In particular, in the field of deep learning, the concepts of network science can be employed to analyze the topological properties and information flow mechanisms of neural networks, thereby guiding model design and architectural improvements to enhance their representational capacity and generalization ability. Therefore, network science plays a vital role in analyzing and managing complex systems in today’s interconnected world.

Topics include, but are not limited to the following:
• Modelling and dynamics analysis of complex networks;
• Estimation and Control of complex networks;
• Multi-layer/time-varying/stochastic complex networks;
• Complex networks appearing in biological, social, engineering, information, etc;
• Computational and data-driven approaches for researching complex networks.

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