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Guo P, Xu Y, Guo S, Tian Y, Sun P. Quasi-critical dynamics in large-scale social systems regulated by sudden events. CHAOS (WOODBURY, N.Y.) 2024; 34:083105. [PMID: 39088345 DOI: 10.1063/5.0218422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Accepted: 07/13/2024] [Indexed: 08/03/2024]
Abstract
How do heterogeneous individual behaviors arise in response to sudden events and how do they shape large-scale social dynamics? Based on a five-year naturalistic observation of individual purchasing behaviors, we extract the long-term consumption dynamics of diverse commodities from approximately 2.2 million purchase orders. We subdivide the consumption dynamics into trend, seasonal, and random components and analyze them using a renormalization group. We discover that the coronavirus pandemic, a sudden event acting on the social system, regulates the scaling and criticality of consumption dynamics. On a large time scale, the long-term dynamics of the system, regardless of arising from trend, seasonal, or random individual behaviors, is pushed toward a quasi-critical region between independent (i.e., the consumption behaviors of different commodities are irrelevant) and correlated (i.e., the consumption behaviors of different commodities are interrelated) phases as the pandemic erupts. On a small time scale, short-term consumption dynamics exhibits more diverse responses to the pandemic. While the trend and random behaviors of individuals are driven to quasi-criticality and exhibit scale-invariance as the pandemic breaks out, seasonal behaviors are more robust against regulations. Overall, these discoveries provide insights into how quasi-critical macroscopic dynamics emerges in heterogeneous social systems to enhance system reactivity to sudden events while there may exist specific system components maintaining robustness as a reflection of system stability.
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Affiliation(s)
- Peng Guo
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
| | - Yunhui Xu
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
| | - Shichun Guo
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China
| | - Yang Tian
- Laboratory of Computational Biology and Complex Systems, City University of Macau, Macau 999078, China
- Faculty of Health and Wellness, City University of Macau, Macau 999078, China
- Faculty of Data Science, City University of Macau, Macau 999078, China
| | - Pei Sun
- Laboratory of Computational Biology and Complex Systems, City University of Macau, Macau 999078, China
- Faculty of Health and Wellness, City University of Macau, Macau 999078, China
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Cencetti G, Contreras DA, Mancastroppa M, Barrat A. Distinguishing Simple and Complex Contagion Processes on Networks. PHYSICAL REVIEW LETTERS 2023; 130:247401. [PMID: 37390429 DOI: 10.1103/physrevlett.130.247401] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/25/2023] [Accepted: 05/17/2023] [Indexed: 07/02/2023]
Abstract
Contagion processes on networks, including disease spreading, information diffusion, or social behaviors propagation, can be modeled as simple contagion, i.e., as a contagion process involving one connection at a time, or as complex contagion, in which multiple interactions are needed for a contagion event. Empirical data on spreading processes, however, even when available, do not easily allow us to uncover which of these underlying contagion mechanisms is at work. We propose a strategy to discriminate between these mechanisms upon the observation of a single instance of a spreading process. The strategy is based on the observation of the order in which network nodes are infected, and on its correlations with their local topology: these correlations differ between processes of simple contagion, processes involving threshold mechanisms, and processes driven by group interactions (i.e., by "higher-order" mechanisms). Our results improve our understanding of contagion processes and provide a method using only limited information to distinguish between several possible contagion mechanisms.
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Affiliation(s)
| | - Diego Andrés Contreras
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
| | - Marco Mancastroppa
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
| | - Alain Barrat
- Aix-Marseille Univ, Université de Toulon, CNRS, Centre de Physique Théorique, Turing Center for Living Systems, Marseille, France
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Patwardhan S, Radicchi F, Fortunato S. Influence maximization: Divide and conquer. Phys Rev E 2023; 107:054306. [PMID: 37329077 DOI: 10.1103/physreve.107.054306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 05/03/2023] [Indexed: 06/18/2023]
Abstract
The problem of influence maximization, i.e., finding the set of nodes having maximal influence on a network, is of great importance for several applications. In the past two decades, many heuristic metrics to spot influencers have been proposed. Here, we introduce a framework to boost the performance of such metrics. The framework consists in dividing the network into sectors of influence, and then selecting the most influential nodes within these sectors. We explore three different methodologies to find sectors in a network: graph partitioning, graph hyperbolic embedding, and community structure. The framework is validated with a systematic analysis of real and synthetic networks. We show that the gain in performance generated by dividing a network into sectors before selecting the influential spreaders increases as the modularity and heterogeneity of the network increase. Also, we show that the division of the network into sectors can be efficiently performed in a time that scales linearly with the network size, thus making the framework applicable to large-scale influence maximization problems.
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Affiliation(s)
- Siddharth Patwardhan
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA
| | - Filippo Radicchi
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA
| | - Santo Fortunato
- Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, USA
- Indiana University Network Science Institute (IUNI), Indiana Univeristy, Bloomington, Indiana 47408, USA
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Cao R, Liu XF, Fang Z, Xu XK, Wang X. How do scientific papers from different journal tiers gain attention on social media? Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Vezzani A, Muñoz MA, Burioni R. Anomalous finite-size scaling in higher-order processes with absorbing states. Phys Rev E 2023; 107:014105. [PMID: 36797930 DOI: 10.1103/physreve.107.014105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 12/21/2022] [Indexed: 01/09/2023]
Abstract
Here we study standard and higher-order birth-death processes on fully connected networks, within the perspective of large-deviation theory [also referred to as the Wentzel-Kramers-Brillouin (WKB) method in some contexts]. We obtain a general expression for the leading and next-to-leading terms of the stationary probability distribution of the fraction of "active" sites as a function of parameters and network size N. We reproduce several results from the literature and, in particular, we derive all the moments of the stationary distribution for the q-susceptible-infected-susceptible (q-SIS) model, i.e., a high-order epidemic model requiring q active ("infected") sites to activate an additional one. We uncover a very rich scenario for the fluctuations of the fraction of active sites, with nontrivial finite-size-scaling properties. In particular, we show that the variance-to-mean ratio diverges at criticality for [1≤q≤3], with a maximal variability at q=2, confirming that complex-contagion processes can exhibit peculiar scaling features including wild variability. Moreover, the leading order in a large-deviation approach does not suffice to describe them: next-to-leading terms are essential to capture the intrinsic singularity at the origin of systems with absorbing states. Some possible extensions of this work are also discussed.
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Affiliation(s)
- Alessandro Vezzani
- Istituto dei Materiali per l'Elettronica ed il Magnetismo (IMEM-CNR), Parco Area delle Scienze, 37/A-43124 Parma, Italy; Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università degli Studi di Parma, Parco Area delle Scienze 7/A, 43124 Parma, Italy; and INFN, Gruppo Collegato di Parma, Parco Area delle Scienze 7/A, 43124 Parma, Italy
| | - Miguel A Muñoz
- Departamento de Electromagnetismo y Física de la Materia and Instituto Carlos I de Física Teórica y Computacional, Universidad de Granada. E-18071 Granada, Spain
| | - Raffaella Burioni
- Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università degli Studi di Parma, Parco Area delle Scienze 7/A, 43124 Parma, Italy and INFN, Gruppo Collegato di Parma, Parco Area delle Scienze 7/A, 43124 Parma, Italy
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