Zhaohua Lin, Lilei Han, Mi Feng, Ying Liu, Ming Tang
{"title":"简单复合物中的高阶非马尔可夫社会传染病","authors":"Zhaohua Lin, Lilei Han, Mi Feng, Ying Liu, Ming Tang","doi":"10.1038/s42005-024-01666-x","DOIUrl":null,"url":null,"abstract":"Higher-order structures such as simplicial complexes are ubiquitous in numerous real-world networks. Empirical evidence reveals that interactions among nodes occur not only through edges but also through higher-dimensional simplicial structures such as triangles. Nevertheless, classic models such as the threshold model fail to capture group interactions within these higher-order structures. In this paper, we propose a higher-order non-Markovian social contagion model, considering both higher-order interactions and the non-Markovian characteristics of real-world spreading processes. We develop a mean-field theory to describe its evolutionary dynamics. Simulation results reveal that the theory is capable of predicting the steady state of the model. Our theoretical analyses indicate that there is an equivalence between the higher-order non-Markovian and the higher-order Markovian social contagions. Besides, we find that non-Markovian recovery can boost the system resilience to withstand a large-scale infection or a small-scale infection under different conditions. This work deepens our understanding of the behaviors of higher-order non-Markovian social contagions in the real world. High-order structures are ubiquitous in numerous real-world networks and play a significant role in social contagion phenomena, the authors introduce a novel higher-order non-Markovian social contagion model, addressing limitations of traditional models. Through mean-field theory and simulations, the authors demonstrate that there is an equivalence between the higher-order non-Markovian and the higher-order Markovian social contagions and reveal the resilience enhancement conferred by non-Markovian recovery, shedding light on real-world contagion dynamics.","PeriodicalId":10540,"journal":{"name":"Communications Physics","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42005-024-01666-x.pdf","citationCount":"0","resultStr":"{\"title\":\"Higher-order non-Markovian social contagions in simplicial complexes\",\"authors\":\"Zhaohua Lin, Lilei Han, Mi Feng, Ying Liu, Ming Tang\",\"doi\":\"10.1038/s42005-024-01666-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Higher-order structures such as simplicial complexes are ubiquitous in numerous real-world networks. Empirical evidence reveals that interactions among nodes occur not only through edges but also through higher-dimensional simplicial structures such as triangles. Nevertheless, classic models such as the threshold model fail to capture group interactions within these higher-order structures. In this paper, we propose a higher-order non-Markovian social contagion model, considering both higher-order interactions and the non-Markovian characteristics of real-world spreading processes. We develop a mean-field theory to describe its evolutionary dynamics. Simulation results reveal that the theory is capable of predicting the steady state of the model. Our theoretical analyses indicate that there is an equivalence between the higher-order non-Markovian and the higher-order Markovian social contagions. Besides, we find that non-Markovian recovery can boost the system resilience to withstand a large-scale infection or a small-scale infection under different conditions. This work deepens our understanding of the behaviors of higher-order non-Markovian social contagions in the real world. High-order structures are ubiquitous in numerous real-world networks and play a significant role in social contagion phenomena, the authors introduce a novel higher-order non-Markovian social contagion model, addressing limitations of traditional models. Through mean-field theory and simulations, the authors demonstrate that there is an equivalence between the higher-order non-Markovian and the higher-order Markovian social contagions and reveal the resilience enhancement conferred by non-Markovian recovery, shedding light on real-world contagion dynamics.\",\"PeriodicalId\":10540,\"journal\":{\"name\":\"Communications Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.nature.com/articles/s42005-024-01666-x.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.nature.com/articles/s42005-024-01666-x\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications Physics","FirstCategoryId":"101","ListUrlMain":"https://www.nature.com/articles/s42005-024-01666-x","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
Higher-order non-Markovian social contagions in simplicial complexes
Higher-order structures such as simplicial complexes are ubiquitous in numerous real-world networks. Empirical evidence reveals that interactions among nodes occur not only through edges but also through higher-dimensional simplicial structures such as triangles. Nevertheless, classic models such as the threshold model fail to capture group interactions within these higher-order structures. In this paper, we propose a higher-order non-Markovian social contagion model, considering both higher-order interactions and the non-Markovian characteristics of real-world spreading processes. We develop a mean-field theory to describe its evolutionary dynamics. Simulation results reveal that the theory is capable of predicting the steady state of the model. Our theoretical analyses indicate that there is an equivalence between the higher-order non-Markovian and the higher-order Markovian social contagions. Besides, we find that non-Markovian recovery can boost the system resilience to withstand a large-scale infection or a small-scale infection under different conditions. This work deepens our understanding of the behaviors of higher-order non-Markovian social contagions in the real world. High-order structures are ubiquitous in numerous real-world networks and play a significant role in social contagion phenomena, the authors introduce a novel higher-order non-Markovian social contagion model, addressing limitations of traditional models. Through mean-field theory and simulations, the authors demonstrate that there is an equivalence between the higher-order non-Markovian and the higher-order Markovian social contagions and reveal the resilience enhancement conferred by non-Markovian recovery, shedding light on real-world contagion dynamics.
期刊介绍:
Communications Physics is an open access journal from Nature Research publishing high-quality research, reviews and commentary in all areas of the physical sciences. Research papers published by the journal represent significant advances bringing new insight to a specialized area of research in physics. We also aim to provide a community forum for issues of importance to all physicists, regardless of sub-discipline.
The scope of the journal covers all areas of experimental, applied, fundamental, and interdisciplinary physical sciences. Primary research published in Communications Physics includes novel experimental results, new techniques or computational methods that may influence the work of others in the sub-discipline. We also consider submissions from adjacent research fields where the central advance of the study is of interest to physicists, for example material sciences, physical chemistry and technologies.