Fundamental Network Theory

The Fundamental Network Theory research area develops core theoretical frameworks, probabilistic models, and computational methods for understanding the structure, function, and dynamics of complex networks. This includes advancements in theoretical network science via maximum entropy random graphs, models of physical and temporal networks, latent network geometry, graphons and graph limits, topological data analysis, higher-order interactions, graph embedding, and scalable graph mining algorithms. Albeit primarily being of a theoretical nature, research results are often systematically validated against real-world network data across diverse applications, ensuring that theoretical insights translate into practical methodologies for complex networked systems.

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