Interplay of simplicial awareness contagion and epidemic spreading on time-varying multiplex networks DOI
Huan Wang, Haifeng Zhang, Peican Zhu

et al.

Chaos An Interdisciplinary Journal of Nonlinear Science, Journal Year: 2022, Volume and Issue: 32(8)

Published: Aug. 1, 2022

There has been growing interest in exploring the dynamical interplay of epidemic spreading and awareness diffusion within multiplex network framework. Recent studies have demonstrated that pairwise interactions are not enough to characterize social contagion processes, but complex mechanisms influence reinforcement should be considered. Meanwhile, physical interaction individuals is static time-varying. Therefore, we propose a novel sUAU-tSIS model simplicial on time-varying networks, which one layer with 2-simplicial complexes considered virtual information address other memory effects treated as contact mimic temporal pattern among population. The microscopic Markov chain approach based theoretical analysis developed, threshold also derived. experimental results show our method good agreement Monte Carlo simulations. Specifically, find synergistic mechanism coming from group promotes awareness, leading suppression epidemics. Furthermore, illustrate capacity individuals, activity heterogeneity, strength play important roles two dynamics; interestingly, crossover phenomenon can observed when investigating heterogeneity strength.

Language: Английский

The physics of higher-order interactions in complex systems DOI
Federico Battiston, Enrico Amico, Alain Barrat

et al.

Nature Physics, Journal Year: 2021, Volume and Issue: 17(10), P. 1093 - 1098

Published: Oct. 1, 2021

Language: Английский

Citations

550
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
Higher-Order Networks DOI Creative Commons
Ginestra Bianconi

Published: Nov. 23, 2021

Higher-order networks describe the many-body interactions of a large variety complex systems, ranging from brain to collaboration networks. Simplicial complexes are generalized network structures which allow us capture combinatorial properties, topology and geometry higher-order Having been used extensively in quantum gravity discrete or discretized space-time, simplicial have only recently started becoming representation choice for capturing underlying systems. This Element provides an in-depth introduction very hot topic theory, covering wide range subjects emergent hyperbolic topological data analysis dynamics. Elements aims demonstrate that provide general mathematical framework reveal how dynamics depends on geometry.

Language: Английский

Citations

135
Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes DOI Creative Commons
Yuanzhao Zhang, Maxime Lucas, Federico Battiston

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: March 23, 2023

Abstract Higher-order networks have emerged as a powerful framework to model complex systems and their collective behavior. Going beyond pairwise interactions, they encode structured relations among arbitrary numbers of units through representations such simplicial complexes hypergraphs. So far, the choice between hypergraphs has often been motivated by technical convenience. Here, using synchronization an example, we demonstrate that effects higher-order interactions are highly representation-dependent. In particular, typically enhance in but opposite effect complexes. We provide theoretical insight linking synchronizability different hypergraph structures (generalized) degree heterogeneity cross-order correlation, which turn influence wide range dynamical processes from contagion diffusion. Our findings reveal hidden impact on dynamics, highlighting importance choosing appropriate when studying with nonpairwise interactions.

Language: Английский

Citations

108
Higher-order motif analysis in hypergraphs DOI Creative Commons
Quintino Francesco Lotito, Federico Musciotto, Alberto Montresor

et al.

Communications Physics, Journal Year: 2022, Volume and Issue: 5(1)

Published: April 5, 2022

Abstract A deluge of new data on real-world networks suggests that interactions among system units are not limited to pairs, but often involve a higher number nodes. To properly encode higher-order interactions, richer mathematical frameworks such as hypergraphs needed, where hyperedges describe an arbitrary Here we systematically investigate motifs, defined small connected subgraphs in which vertices may be linked by any order, and propose efficient algorithm extract complete motif profiles from empirical data. We identify different families hypergraphs, characterized distinct connectivity patterns at the local scale. also set measures study nested structure provide evidences structural reinforcement, mechanism associates strengths for nodes interact more pairwise level. Our work highlights informative power providing principled way fingerprints network microscale.

Language: Английский

Citations

91
Epidemic spreading on higher-order networks DOI
Wei Wang, Yanyi Nie, Wenyao Li

et al.

Physics Reports, Journal Year: 2024, Volume and Issue: 1056, P. 1 - 70

Published: Jan. 19, 2024

Language: Английский

Citations

66
Reinforcement learning and collective cooperation on higher-order networks DOI
Yan Xu, Juan Wang, Jiaxing Chen

et al.

Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 301, P. 112326 - 112326

Published: Aug. 6, 2024

Language: Английский

Citations

17
Universal Nonlinear Infection Kernel from Heterogeneous Exposure on Higher-Order Networks DOI Creative Commons
Guillaume St-Onge, Hanlin Sun, Antoine Allard

et al.

Physical Review Letters, Journal Year: 2021, Volume and Issue: 127(15)

Published: Oct. 6, 2021

The collocation of individuals in different environments is an important prerequisite for exposure to infectious diseases on a social network. Standard epidemic models fail capture the potential complexity this scenario by (1) neglecting higher-order structure contacts that typically occur through like workplaces, restaurants, and households, (2) assuming linear relationship between infected risk infection. Here, we leverage hypergraph model embrace heterogeneity individual participation these environments. We find combining heterogeneous with concept minimal infective dose induces universal nonlinear infection risk. Under kernels, conventional wisdom breaks down emergence discontinuous transitions, superexponential spread, hysteresis.

Language: Английский

Citations

79
Simplicial contagion in temporal higher-order networks DOI Creative Commons
Sandeep Chowdhary, Aanjaneya Kumar, Giulia Cencetti

et al.

Journal of Physics Complexity, Journal Year: 2021, Volume and Issue: 2(3), P. 035019 - 035019

Published: July 8, 2021

Complex networks represent the natural backbone to study epidemic processes in populations of interacting individuals. Such a modeling framework, however, is naturally limited pairwise interactions, making it less suitable properly describe social contagion, where individuals acquire new norms or ideas after simultaneous exposure multiple sources infections. Simplicial contagion has been proposed as an alternative framework simplices are used encode group interactions any order. The presence higher-order leads explosive transitions and bistability which cannot be obtained when only dyadic ties considered. In particular, critical mass effects can emerge even for infectivity values below standard threshold, size initial seed infectious nodes determines whether system would eventually fall endemic healthy state. Here we extend simplicial time-varying networks, created destroyed over time. By following microscopic Markov chain approach, find that same might not lead stationary state, depending on temporal properties underlying network structure, show persistent anticipate onset state finite-size systems. We characterize this behavior with prescribed correlation between consecutive heterogeneous complexes, showing temporality again limits effect spreading, but pronounced way than homogeneous structures. Our work suggests importance incorporating temporality, realistic feature many real-world systems, into investigation dynamical beyond interactions.

Language: Английский

Citations

73
Inference of hyperedges and overlapping communities in hypergraphs DOI Creative Commons
Martina Contisciani, Federico Battiston, Caterina De Bacco

et al.

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Nov. 24, 2022

Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. Here we propose framework based on statistical inference characterize the structural organization hypergraphs. The method allows infer missing hyperedges size in principled way, jointly detect overlapping communities presence higher-order interactions. Furthermore, our model has an efficient numerical implementation, it runs faster than dyadic algorithms pairwise records projected from data. We apply variety systems, showing strong performance hyperedge prediction tasks, detecting well aligned with information carried by interactions, robustness against addition noisy hyperedges. Our approach illustrates fundamental advantages hypergraph probabilistic when modeling relational systems

Language: Английский

Citations

57