Emergence of Geometric Turing Patterns in Complex Networks

Physical Review E

Physica A: Statistical Mechanics and its Applications, 2004

arXiv (Cornell University), 2021

In this paper, the Turing instability in reaction-diffusion models defined on complex networks is studied. Here, we focus on three types of models which generate complex networks, i.e. the Erd\H{o}s-R\'enyi, the Watts-Strogatz and the threshold network models. From analysis of the Laplacian matrices of graphs generated by these models, we reveal that the stable-unstable regions of a spatially homogeneous solution completely differ, depending on network structures. In particular, we approximately argue the existence of the stable-unstable regions in the cases of regular enhanced ring lattices which include regular circles, and networks generated by the threshold network model when the number of vertices is large enough.

Physical Review E, 2018

Computing Research Repository, 2001

Physical Review E, 2016

Communications Physics

Clustering–the tendency for neighbors of nodes to be connected–quantifies the coupling of a complex network to its latent metric space. In random geometric graphs, clustering undergoes a continuous phase transition, separating a phase with finite clustering from a regime where clustering vanishes in the thermodynamic limit. We prove this geometric to non-geometric phase transition to be topological in nature, with anomalous features such as diverging entropy as well as atypical finite-size scaling behavior of clustering. Moreover, a slow decay of clustering in the non-geometric phase implies that some real networks with relatively high levels of clustering may be better described in this regime.

International Journal of Bifurcation and Chaos, 2010

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