Exploring complex networks via topological embedding on surfaces
Tomaso Aste, Ruggero Gramatica, T. Di Matteo

TL;DR
This paper explores how embedding graphs on surfaces of varying genus influences their structural properties, revealing a versatile approach to generate and analyze complex networks with different global and local characteristics.
Contribution
It introduces a topological embedding framework that allows for the generation and characterization of networks with tunable properties based on surface genus, including a statistical mechanics approach.
Findings
Surface genus significantly affects degree distribution and clustering.
Global network properties change from large-world to small-world with increasing genus.
Cooling dynamics exhibit glassy-like freezing transitions influenced by surface topology.
Abstract
We demonstrate that graphs embedded on surfaces are a powerful and practical tool to generate, characterize and simulate networks with a broad range of properties. Remarkably, the study of topologically embedded graphs is non-restrictive because any network can be embedded on a surface with sufficiently high genus. The local properties of the network are affected by the surface genus which, for example, produces significant changes in the degree distribution and in the clustering coefficient. The global properties of the graph are also strongly affected by the surface genus which is constraining the degree of interwoveness, changing the scaling properties from large-world-kind (small genus) to small- and ultra-small-world-kind (large genus). Two elementary moves allow the exploration of all networks embeddable on a given surface and naturally introduce a tool to develop a statistical…
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Taxonomy
TopicsComplex Network Analysis Techniques · Topological and Geometric Data Analysis · Data Visualization and Analytics
