Hybrid universality classes of systemic cascades DOI Creative Commons
Ivan Bonamassa, Bnaya Gross, János Kertész

et al.

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

Published: Feb. 6, 2025

Cascades are self-reinforcing processes underlying the systemic risk of many complex systems. Understanding universal aspects these phenomena is fundamental interest, yet typically bound to numerical observations in ad-hoc models and limited insights. Here, we develop a unifying approach that reveals two distinct universality classes cascades determined by global symmetry cascading process. We provide hyperscaling arguments predicting hybrid critical characterized combination both mean-field spinodal exponents d-dimensional corrections, show how parity invariance influences geometry lifetime avalanches. Our theory applies wide range networked systems arbitrary dimensions, as demonstrate simulations encompassing classic novel cascade models, revealing principles amenable experimental validation. Cascading accompanied mixed-order transitions characteristic for ecological, financial, earth, climate, social To better understand mechanisms behind, authors uncover cascades, symmetries

Language: Английский

Dynamics on higher-order networks: a review DOI Creative Commons
Soumen Majhi, Matjaž Perc, Dibakar Ghosh

et al.

Journal of The Royal Society Interface, Journal Year: 2022, Volume and Issue: 19(188)

Published: March 1, 2022

Network science has evolved into an indispensable platform for studying complex systems. But recent research identified limits of classical networks, where links connect pairs nodes, to comprehensively describe group interactions. Higher-order a link can more than two have therefore emerged as new frontier in network science. Since interactions are common social, biological and technological systems, higher-order networks recently led important discoveries across many fields research. Here, we review these works, focusing particular on the novel aspects dynamics that emerges networks. We cover variety dynamical processes thus far been studied, including different synchronization phenomena, contagion processes, evolution cooperation consensus formation. also outline open challenges promising directions future

Language: Английский

Citations

329
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

174
Controlling complex networks with complex nodes DOI
Raissa M. D’Souza, Mario di Bernardo, Yang‐Yu Liu

et al.

Nature Reviews Physics, Journal Year: 2023, Volume and Issue: 5(4), P. 250 - 262

Published: March 24, 2023

Language: Английский

Citations

64
Neuroscience Needs Network Science DOI Creative Commons
Dániel L. Barabási, Ginestra Bianconi, Edward T. Bullmore

et al.

Journal of Neuroscience, Journal Year: 2023, Volume and Issue: 43(34), P. 5989 - 5995

Published: Aug. 23, 2023

The brain is a complex system comprising myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as powerful tool for studying such intricate systems, offering framework integrating multiscale data complexity. Here, we discuss the application network study brain, addressing topics models metrics, connectome, role dynamics neural networks. We explore opportunities multiple streams transitions from development to healthy function disease, potential collaboration between neuroscience communities. underscore importance fostering interdisciplinary through funding initiatives, workshops, conferences, well supporting students postdoctoral fellows with interests both disciplines. By uniting communities, can develop novel network-based methods tailored circuits, paving way towards deeper functions.

Language: Английский

Citations

56
Graph Filters for Signal Processing and Machine Learning on Graphs DOI
Elvin Isufi, Fernando Gama, David I Shuman

et al.

IEEE Transactions on Signal Processing, Journal Year: 2024, Volume and Issue: 72, P. 4745 - 4781

Published: Jan. 1, 2024

Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters the crux of many signal processing machine learning techniques, including convolutional neural networks. Increasingly, modern also networks other irregular domains whose structure is better captured by a graph. To process learn such data, graph account for underlying domain. In this article, we provide comprehensive overview filters, different filtering categories, design strategies each type, trade-offs between types filters. We discuss how to extend into filter banks enhance representational power; is, model broader variety classes, patterns, relationships. showcase role applications. Our aim article provides unifying framework both beginner experienced researchers, as well common understanding promotes collaborations at intersections processing, learning, application domains.

Language: Английский

Citations

34
Collective dynamics of swarmalators with higher-order interactions DOI Creative Commons
Md Sayeed Anwar, Gourab Kumar Sar, Matjaž Perc

et al.

Communications Physics, Journal Year: 2024, Volume and Issue: 7(1)

Published: Feb. 21, 2024

Language: Английский

Citations

31
The low-rank hypothesis of complex systems DOI
Vincent Thibeault, Antoine Allard, Patrick Desrosiers

et al.

Nature Physics, Journal Year: 2024, Volume and Issue: 20(2), P. 294 - 302

Published: Jan. 10, 2024

Language: Английский

Citations

27
Theory of percolation on hypergraphs DOI Creative Commons
Ginestra Bianconi, S. N. Dorogovt︠s︡ev

Physical review. E, Journal Year: 2024, Volume and Issue: 109(1)

Published: Jan. 17, 2024

Hypergraphs capture the higher-order interactions in complex systems and always admit a factor graph representation, consisting of bipartite network nodes hyperedges. As hypegraphs are ubiquitous, investigating hypergraph robustness is problem major research interest. In literature hypergraphs so far only has been treated adopting factor-graph percolation, which describes well remain functional even after removal one more their nodes. This approach, however, fall short to describe situations fail when any removed, this latter scenario applying, for instance, supply chains, catalytic networks, protein-interaction networks chemical reactions, etc. Here we show that these cases correct process investigate with distinct from percolation. We build message-passing theory its critical behavior using generating function formalism supported by Monte Carlo simulations on random real data. Notably, node percolation threshold exceeds graphs. Furthermore differently what happens ordinary graphs, hyperedge do not coincide, exceeding threshold. These results demonstrate fat-tailed cardinality distribution hyperedges cannot lead hyper-resilience phenomenon contrast where divergent second moment guarantees zero

Language: Английский

Citations

21
Topology shapes dynamics of higher-order networks DOI
Ana P. Millán, Hanlin Sun, Lorenzo Giambagli

et al.

Nature Physics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

Language: Английский

Citations

2
Weighted simplicial complexes and their representation power of higher-order network data and topology DOI
Federica Baccini, Filippo Geraci, Ginestra Bianconi

et al.

Physical review. E, Journal Year: 2022, Volume and Issue: 106(3)

Published: Sept. 26, 2022

Hypergraphs and simplical complexes both capture the higher-order interactions of complex systems, ranging from collaboration networks to brain networks. One open problem in field is what should drive choice adopted mathematical framework describe starting data interactions. Unweighted simplicial typically involve a loss information data, though having benefit topology data. In this work we show that weighted allow circumvent all limitations unweighted represent particular, can without information, allowing at same time The probed by studying spectral properties suitably defined Hodge Laplacians displaying normalized spectrum. spectrum (weighted) here studied combining cohomology theory with theory. proposed framework, quantify compare content spectra different dimension using entropies relative entropies. methodology tested on real version model "Network Geometry Flavor".

Language: Английский

Citations

56