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

An efficient adaptive degree-based heuristic algorithm for influence maximization in hypergraphs DOI
Ming Xie, Xiu-Xiu Zhan, Chuang Liu

et al.

Information Processing & Management, Journal Year: 2022, Volume and Issue: 60(2), P. 103161 - 103161

Published: Nov. 25, 2022

Language: Английский

Citations

55
Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hypergraphs DOI Creative Commons
Guillaume St-Onge, Iacopo Iacopini, Vito Latora

et al.

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

Published: Jan. 17, 2022

Abstract Contagion phenomena are often the results of multibody interactions—such as superspreading events or social reinforcement—describable hypergraphs. We develop an approximate master equation framework to study contagions on hypergraphs with a heterogeneous structure in terms group size (hyperedge cardinality) and node membership (hyperdegree). By mapping interactions nonlinear infection rates, we demonstrate influence large groups two ways. First, characterize phase transition, which can be continuous discontinuous bistable regime. Our analytical expressions for critical tricritical points highlight first three moments distribution. also show that sizes contagion promote mesoscopic localization regime where is sustained by largest groups, thereby inhibiting bistability. Second, formulate optimal seeding problem hypergraph compare strategies: allocating seeds according properties. find that, when sufficiently nonlinear, more effective than individual hubs.

Language: Английский

Citations

53
Group interactions modulate critical mass dynamics in social convention DOI Creative Commons
Iacopo Iacopini, Giovanni Petri, Andrea Baronchelli

et al.

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

Published: March 18, 2022

Abstract How can minorities of individuals overturn social conventions? The theory critical mass states that when a committed minority reaches size, cascade behavioural changes occur, overturning apparently stable norms. Evidence comes from theoretical and empirical studies in which very different sizes, including extremely small ones, manage to bring system its tipping point. Here, we explore this diversity scenarios by introducing group interactions as crucial element realism into model for convention. We find the necessary trigger behaviour change be if have limited propensity their views. Moreover, ability existing norms depends complex way on size. Our findings reconcile sizes found previous investigations unveil role groups such processes. This further highlights importance emerging field higher-order networks, beyond pairwise interactions.

Language: Английский

Citations

41
Multistability, intermittency, and hybrid transitions in social contagion models on hypergraphs DOI Creative Commons
Guilherme Ferraz de Arruda, Giovanni Petri, Pablo M. Rodríguez

et al.

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

Published: March 13, 2023

Abstract Although ubiquitous, interactions in groups of individuals are not yet thoroughly studied. Frequently, single modeled as critical-mass dynamics, which is a widespread concept used only by academics but also politicians and the media. However, less explored questions how collection will behave their intersection might change dynamics. Here, we formulate this process binary-state dynamics on hypergraphs. We showed that our model has rich behavior beyond discontinuous transitions. Notably, have multistability intermittency. demonstrated phenomenology could be associated with community structures, where or intermittency controlling number size bridges between communities. Furthermore, provided evidence observed transitions hybrid. Our findings open new paths for research, ranging from physics, formal calculation quantities interest, to social sciences, experiments can designed.

Language: Английский

Citations

41
Community detection in large hypergraphs DOI Creative Commons
Nicolò Ruggeri, Martina Contisciani, Federico Battiston

et al.

Science Advances, Journal Year: 2023, Volume and Issue: 9(28)

Published: July 12, 2023

Hypergraphs, describing networks where interactions take place among any number of units, are a natural tool to model many real-world social and biological systems. Here, we propose principled framework the organization higher-order data. Our approach recovers community structure with accuracy exceeding that currently available state-of-the-art algorithms, as tested in synthetic benchmarks both hard overlapping ground-truth partitions. is flexible allows capturing assortative disassortative structures. Moreover, our method scales orders magnitude faster than competing making it suitable for analysis very large hypergraphs, containing millions nodes thousands nodes. work constitutes practical general hypergraph analysis, broadening understanding

Language: Английский

Citations

35
Non-linear consensus dynamics on temporal hypergraphs with random noisy higher-order interactions DOI Creative Commons
Yilun Shang

Journal of Complex Networks, Journal Year: 2023, Volume and Issue: 11(2)

Published: Feb. 23, 2023

Abstract Complex networks encoding the topological architecture of real-world complex systems have recently been undergoing a fundamental transition beyond pairwise interactions described by dyadic connections among nodes. Higher-order structures such as hypergraphs and simplicial complexes utilized to model group for varied networked from brain, society, biological physical systems. In this article, we investigate consensus dynamics over temporal featuring non-linear modulating functions, time-dependent topology random perturbations. Based upon analytical tools in matrix, hypergraph, stochastic process real analysis, establish sufficient conditions all nodes network reach sense almost sure convergence $\mathscr{L}^2$ convergence. The rate moments equilibrium determined. Our results offer theoretical foundation recent series numerical studies observations multi-body dynamical

Language: Английский

Citations

25
Contagion dynamics on higher-order networks DOI
Guilherme Ferraz de Arruda, Alberto Aleta, Yamir Moreno

et al.

Nature Reviews Physics, Journal Year: 2024, Volume and Issue: 6(8), P. 468 - 482

Published: July 5, 2024

Language: Английский

Citations

16
Global synchronization on time-varying higher-order structures DOI Creative Commons
Md Sayeed Anwar, Dibakar Ghosh, Timotéo Carletti

et al.

Journal of Physics Complexity, Journal Year: 2024, Volume and Issue: 5(1), P. 015020 - 015020

Published: March 1, 2024

Abstract Synchronization has received a lot of attention from the scientific community for systems evolving on static networks or higher-order structures, such as hypergraphs and simplicial complexes. In many relevant real-world applications, latter are not but do evolve in time, this work we thus discuss impact time-varying nature structures emergence global synchronization. To achieve goal, extend master stability formalism to account, general way, additional contributions arising time evolution structure supporting dynamical systems. The theory is successfully challenged against two illustrative examples, Stuart–Landau nonlinear oscillator Lorenz chaotic oscillator.

Language: Английский

Citations

13
The temporal dynamics of group interactions in higher-order social networks DOI Creative Commons
Iacopo Iacopini, Màrton Karsai, Alain Barrat

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Aug. 27, 2024

Representing social systems as networks, starting from the interactions between individuals, sheds light on mechanisms governing their dynamics. However, networks encode only pairwise interactions, while most occur among groups of requiring higher-order network representations. Despite recent interest in little is known about that govern formation and evolution groups, how people move groups. Here, we leverage empirical data children university students to study temporal dynamics at both individual group levels, characterising individuals navigate form disaggregate. We find robust patterns across contexts propose a dynamical model closely reproduces observations. These results represent further step understanding systems, open up research directions impact processes evolve top them. The structure many where human involve communities can be described by networks. authors hypergraph-based describes different sizes.

Language: Английский

Citations

11
Higher-order correlations reveal complex memory in temporal hypergraphs DOI Creative Commons
Luca Gallo, Lucas Lacasa, Vito Latora

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: June 4, 2024

Abstract Many real-world complex systems are characterized by interactions in groups that change time. Current temporal network approaches, however, unable to describe group dynamics, as they based on pairwise only. Here, we use time-varying hypergraphs such systems, and introduce a framework higher-order correlations characterize their organization. The analysis of human interaction data reveals the existence coherent interdependent mesoscopic structures, thus capturing aggregation, fragmentation nucleation processes social systems. We model with non-Markovian interactions, which memory fundamental mechanism underlying emerging pattern data.

Language: Английский

Citations

10