About this Research Topic

Background

With a few decades of development, networks have now become a go-to mathematical model for analyzing various complex systems in the real world, whether physical, biological, economical, or social. Researchers from diverse fields have developed a wide range of techniques to address problems in such networks, integrating knowledge from math, physics, computer science, and beyond.

This Research Topic will cover the latest developments of network science from a mathematical perspective but also under the point of view of the applications: in so doing, it aims at providing state-of-the-art tools to a general audience, increasing the dialogue between network scientists with diverse specializations, and fostering the collaboration between methodological researchers in networks theory and those who explore real-world phenomena.

The scope of this research topic includes, but is not limited to:

• Models for dynamics on/of networks.
• Methods for network data analysis such as community detection, link prediction, centrality measurements, and so on.
• Methods for networks beyond simple graphs, such as networks with higher-order structures, multiplex networks, longitudinal networks, etc.
• Methods and models for assessing network resilience.
• Machine learning on networks such as network embedding, graph learning, and knowledge graphs.
• Applications of networks to disciplines such as physics, economics, social science, and so on.

Keywords: complex networks, complex systems, dynamical systems, data science, computational science

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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