Emergence of Geometric Turing Patterns in Complex Networks DOI Creative Commons
Jasper van der Kolk, Guillermo García-Pérez, Nikos E. Kouvaris

et al.

Physical Review X, Journal Year: 2023, Volume and Issue: 13(2)

Published: June 22, 2023

Turing patterns, arising from the interplay between competing species of diffusive particles, has long been an important concept for describing non-equilibrium self-organization in nature, and extensively investigated many chemical biological systems. Historically, these patterns have studied extended systems lattices. Recently, instability was found to produce topological networks with scale-free degree distributions small world property, although apparent absence geometric organization. While hints explicitly simple network models (e.g Watts-Strogatz) found, question exact nature morphology heterogeneous complex remains unresolved. In this work, we study framework random graph models, where topology is explained by underlying space. We demonstrate that not only can be observed, their wavelength also estimated studying eigenvectors annealed Laplacian. Finally, show embeddings real networks. These results indicate there a profound connection function its hidden geometry, even when associated dynamical processes are exclusively determined topology.

Language: Английский

Networks beyond pairwise interactions: Structure and dynamics DOI Creative Commons
Federico Battiston, Giulia Cencetti, Iacopo Iacopini

et al.

Physics Reports, Journal Year: 2020, Volume and Issue: 874, P. 1 - 92

Published: June 13, 2020

The complexity of many biological, social and technological systems stems from the richness interactions among their units. Over past decades, a great variety complex has been successfully described as networks whose interacting pairs nodes are connected by links. Yet, in face-to-face human communication, chemical reactions ecological systems, can occur groups three or more cannot be simply just terms simple dyads. Until recently, little attention devoted to higher-order architecture real systems. However, mounting body evidence is showing that taking structure these into account greatly enhance our modeling capacities help us understand predict emerging dynamical behaviors. Here, we present complete overview field beyond pairwise interactions. We first discuss methods represent give unified presentation different frameworks used describe highlighting links between existing concepts representations. review measures designed characterize models proposed literature generate synthetic structures, such random growing simplicial complexes, bipartite graphs hypergraphs. introduce rapidly research on topology. focus novel emergent phenomena characterizing landmark processes, diffusion, spreading, synchronization games, when extended elucidate relations topology properties, conclude with summary empirical applications, providing an outlook current conceptual frontiers.

Language: Английский

Citations

1153
Robustness and resilience of complex networks DOI
Oriol Artime, Marco Grassia, Manlio De Domenico

et al.

Nature Reviews Physics, Journal Year: 2024, Volume and Issue: 6(2), P. 114 - 131

Published: Jan. 8, 2024

Language: Английский

Citations

157
Brain network communication: concepts, models and applications DOI
Caio Seguin, Olaf Sporns, Andrew Zalesky

et al.

Nature reviews. Neuroscience, Journal Year: 2023, Volume and Issue: 24(9), P. 557 - 574

Published: July 12, 2023

Language: Английский

Citations

129
More is different in real-world multilayer networks DOI
Manlio De Domenico

Nature Physics, Journal Year: 2023, Volume and Issue: 19(9), P. 1247 - 1262

Published: Aug. 28, 2023

Language: Английский

Citations

78
Laplacian renormalization group for heterogeneous networks DOI Creative Commons
Pablo Villegas, Tommaso Gili, Guido Caldarelli

et al.

Nature Physics, Journal Year: 2023, Volume and Issue: 19(3), P. 445 - 450

Published: Jan. 9, 2023

The renormalization group is the cornerstone of modern theory universality and phase transitions, a powerful tool to scrutinize symmetries organizational scales in dynamical systems. However, its network counterpart particularly challenging due correlations between intertwined scales. To date, explorations are based on hidden geometries hypotheses. Here, we propose Laplacian RG diffusion-based picture complex networks, defining both Kadanoff supernodes' concept, momentum space procedure, \emph{\'a la Wilson}, applying this scheme real networks natural parsimonious way.

Language: Английский

Citations

52
Exploring Emergent Properties in Enzymatic Reaction Networks: Design and Control of Dynamic Functional Systems DOI Creative Commons
Souvik Ghosh, Mathieu G. Baltussen, Nikita M. Ivanov

et al.

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(5), P. 2553 - 2582

Published: March 4, 2024

The intricate and complex features of enzymatic reaction networks (ERNs) play a key role in the emergence sustenance life. Constructing such vitro enables stepwise build up complexity introduces opportunity to control activity using physicochemical stimuli. Rational design modulation network motifs enable engineering artificial systems with emergent functionalities. Such functional are useful for variety reasons as creating new-to-nature dynamic materials, producing value-added chemicals, constructing metabolic modules synthetic cells, even enabling molecular computation. In this review, we offer insights into chemical characteristics ERNs while also delving their potential applications associated challenges.

Language: Английский

Citations

21
From the origin of life to pandemics: emergent phenomena in complex systems DOI Creative Commons
Oriol Artime, Manlio De Domenico

Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, Journal Year: 2022, Volume and Issue: 380(2227)

Published: May 23, 2022

When a large number of similar entities interact among each other and with their environment at low scale, unexpected outcomes higher spatio-temporal scales might spontaneously arise. This non-trivial phenomenon, known as emergence, characterizes broad range distinct complex systems-from physical to biological social-and is often related collective behaviour. It ubiquitous, from non-living such oscillators that under specific conditions synchronize, living ones, birds flocking or fish schooling. Despite the ample phenomenological evidence existence systems' emergent properties, central theoretical questions study emergence remain unanswered, lack widely accepted, rigorous definition phenomenon identification essential favour emergence. We offer here general overview sketch current future challenges on topic. Our short review also serves an introduction theme issue

Language: Английский

Citations

38
Diversity of information pathways drives sparsity in real-world networks DOI
Arsham Ghavasieh, Manlio De Domenico

Nature Physics, Journal Year: 2024, Volume and Issue: 20(3), P. 512 - 519

Published: Jan. 17, 2024

Language: Английский

Citations

14
Statistical Mechanics of Directed Networks DOI Creative Commons
Marián Boguñá, M. Ángeles Serrano

Entropy, Journal Year: 2025, Volume and Issue: 27(1), P. 86 - 86

Published: Jan. 18, 2025

Directed networks are essential for representing complex systems, capturing the asymmetry of interactions in fields such as neuroscience, transportation, and social networks. Directionality reveals how influence, information, or resources flow within a network, fundamentally shaping behavior dynamical processes distinguishing directed from their undirected counterparts. Robust null models crucial identifying meaningful patterns these representations, yet designing that preserve key features remains significant challenge. One critical feature is reciprocity, which reflects balance bidirectional provides insights into underlying structural principles shape connectivity. This paper introduces statistical mechanics framework networks, modeling them ensembles interacting fermions. By controlling reciprocity other network properties, our formalism offers principled approach to analyzing structures dynamics, introducing new perspectives analytical tools empirical studies.

Language: Английский

Citations

1
Coarse-graining network flow through statistical physics and machine learning DOI Creative Commons
Zhang Zhang, Arsham Ghavasieh, Jiang Zhang

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 13, 2025

Information dynamics plays a crucial role in complex systems, from cells to societies. Recent advances statistical physics have made it possible capture key network properties, such as flow diversity and signal speed, using entropy free energy. However, large system sizes pose computational challenges. We use graph neural networks identify suitable groups of components for coarse-graining achieve low complexity, practical application. Our approach preserves information even under significant compression, shown through theoretical analysis experiments on synthetic empirical networks. find that the model merges nodes with similar structural suggesting they perform redundant roles transmission. This method enables low-complexity compression extremely networks, offering multiscale perspective biological, social, technological better than existing methods mostly focused structure. are systems. The authors apply group reducing complexity. Their being effective

Language: Английский

Citations

1