Peer relationships are closely associated with the academic performance of adolescent students. This paper develops an integrated framework taking the peer main effect as the starting point to systematically incorporate the demonstration effect, the within-class network effect, and the cross-class average effect. Using comprehensive network and survey data from high school students in a typical county in western China, this paper employs a network-based identification strategy to reveal robust positive peer effects. The mechanism test shows that the demonstration effect can play a moderating role in peer effects on academic performance, with the network effect being heterogeneous across students and the average effect indicative of a potential role in providing diverse academic information. These findings provide empirical insights into the multifaceted nature of peer dynamics, offering actionable evidence for designing targeted interventions to improve educational outcomes in underdeveloped regions.

Peer similarity has long been a central concern in research on social networks and adolescent development. Prior studies have largely focused on similarity in maladaptive peer behaviors, often restricting analysis to a single type of peer relationship and relying on partial network or nomination-based data in adolescent populations. This study examines the presence of peer similarity in psychological distress using whole school social network data from three secondary schools in western China (N = 5161), encompassing five types of peer networks including friend, assistance, confidant, recreational, and academic-support networks. Employing network-appropriate analytical methods QAP and MRQAP, we report the significant similarity in psychological distress across all five network types, with the strongest pattern observed within academic support networks, reflecting the cultural emphasis on academic achievement in the Chinese educational context. In addition, we apply PSM as to estimate associations at the individual level, and the estimated average treatment effects indicate that adolescents embedded in peer contexts characterized by higher levels of psychological distress exhibit significantly higher distress themselves compared to those exposed to lower peer distress. Adopting a social network perspective, our findings demonstrate that adolescents’ psychological distress clusters within school-based peer networks. These results suggest that schools should be conceptualized as key intervention fields and that peer relationships constitute important units for the design and implementation of mental health interventions.

Collective behavior in social systems is shaped not only by pairwise individual-level interactions but also by higher-order groupwise interactions. In this study, we investigate majority-driven collective dynamics within a hypernetwork framework that integrates both pairwise and groupwise interactions. Through systematic simulations across four network topologies, we demonstrate that majority dynamics robustly drive systems from disorder to order, ultimately leading to stable global consensus. By varying pairwise structures, groupwise structures, pairwise-groupwise influence weights, and initial system conditions, we find that the speed and stability of convergence depend strongly on pairwise structures: larger and denser networks converge more smoothly and stably, where random connectivity sustains diversity and delays consensus while structured connectivity promotes strong and resilient consensus. Higher-order groupwise interactions further suppress disagreement and reinforce global consensus. We also identify a fundamental trade-off between pairwise and groupwise influence weights. Dominant pairwise influence accelerate consensus, whereas dominant groupwise influence preserves heterogeneity and slows convergence. In addition, collective outcomes are jointly determined by initial state distributions and individual preferences, giving rise to threshold-like transitions that depend on network topology. Together, these findings reveal how higher-order social interactions reshape majority dynamics and provide a unified framework for understanding consensus formation in complex social systems beyond purely dyadic interactions.

Knowledge diffusion is a critical driver of innovation, and team-based diffusion has increasingly become the dominant mode of knowledge diffusion. However, its efficiency is strongly shaped by inertia mechanisms embedded within teams. To capture this phenomenon, this paper first formalizes inertia mechanisms in networks, distinguishing between temporal inertia and spatial inertia. Building on this foundation, we propose a team inertia network model that integrates both forms of inertia and specify its evolutionary dynamics. Through extensive simulation experiments, we examine how temporal and spatial inertia jointly affect knowledge diffusion. The results indicate that moderate spatial inertia can enhance the average knowledge level of a team, whereas excessive or insufficient spatial inertia reduces diffusion efficiency. The impact of temporal inertia depends on communication density and knowledge dimensionality, where tighter connectivity enables members to gain more balanced benefits, reflected in a more stable Gini coefficient and more stable negative spatial autocorrelation. This work contributes a novel modeling framework for analyzing team knowledge evolution and provides practical insights for designing team structures and communication strategies that leverage inertia mechanisms to improve knowledge sharing, innovation capacity, and collaborative efficiency.

Rapid access to emergency services in metropolitan areas is crucial for urban sustainability. In recent years, the concept of urban resilience has received increasing attention, with emergency medical services (EMS) coverage and accessibility emerging as a crucial component. Taking Shenzhen as an example, this study proposes a framework focusing on the EMS system planning. The demand of EMS isestimated by real-world ambulance dispatch data, and the supply is assessed based on the varying capacity of each EMS station. Based on real-world EMS demand and supply, this study first identifies areas with low EMS accessibility by a Nearest-neighbor Gaussian two-step floating catchment area (NN-Ga2SFCA) method. Taking existing medical institutions within low-EMS-accessibility areas as candidate EMS stations, this study introduces an optimized scheme for EMS stations distribution through the p-median model. Finally, it is found that the accessibility of the EMS in Shenzhen has significantly improved by making a comparative analysis before and after optimization. The framework offers a practical and meaningful procedure to EMS planning. This study can help urban policymakers formulate medical-related policies and serve as a benchmark for optimizing EMS in other megacities.

Tag-based ethnocentrism is the basic mechanism for explaining social identity in human behaviors. Combining game theory with structural balance, the present study proposed a novel evolutionary game model in signed networks considering the tag-mediated effect, which provides a new perspective for explaining collective dynamics in social systems. Experiments show that negative relations promote the unconditional cooperation, and the conditional strategy is less likely to appear in the evolution in signed networks. Network adaption is helpful in reducing the proportion of unconditional defectors, but it can be mediated by the tag-based effect. The unconditional cooperation prevails when the speed of relation updating is faster than that of strategy updating increases to a certain extent. The evolution of structural balance can be capable of reducing the proportion of ethnocentric players. From a global point of view, the tag-mediated effect stimulates the formation of attractors or repeller structures, but the dynamic structural balance prevents the formation.

Cooperation is crucial for social progress but is often undermined by free-riding behavior. This study explores how biased allocation mechanisms affect the evolution of cooperation in public goods games. By incorporating group attributes into a game-theoretic model and designing unequal payoff allocation rules based on majority group status, we simulated evolutionary dynamics on random networks. Results indicate that moderate bias strength and lower majority thresholds significantly promote cooperation, particularly when the public goods enhancement factor is moderate. These findings advance collective action theory by demonstrating the role of structural incentives in fostering cooperation and suggest directions for future empirical research and exploration of diverse network structures.

Human games are inherently diverse, involving more than mere identity interactions. The diversity of game tasks offers a more authentic explanation in the exploration of social dilemmas. Human behavior is also influenced by conformity, and prosociality is a crucial factor in addressing social dilemmas. This study proposes a generalized prisoner’s dilemma model of task diversity that incorporates a conformity-driven interaction. Simulation findings indicate that the diversity of multi-tasks and the path dependence contribute to the flourishing of cooperation in games. Conformity-driven interactions also promote cooperation. However, this promotion effect does not increase linearly, and only appropriate task sizes and suitable proportions of conformity-driven interactions yield optimal results. From a broader group perspective, the interplay of network adaptation, task size, and conformity-driven interaction can form a structure of attractors or repellents.

The logic of collective action has laid a foundation for the research of public choice, and the success of collective action has been a long-term discussion when free-riding mechanism is considered in the dynamics. This study proposes a , which provides a novel dimension for explaining the logic of collective action. Under the framework, the accumulation of early social influence, conformity, and the pressure of relationship updating in small groups is discussed. The experiment results show that the accumulation of early social influence indirectly promotes the participants of collective action; conformity is conducive to stimulating collective action, but relies on the accumulation of early social influence; the pressure of relationship updating plays the small-group role, which promotes the participation of collective actions; all these effects are helpful in forming the cascade of cooperators, and prevent the coexistence of participants and non-participants of collective action.

Social influence has been widely discussed in various disciplines due to its important sociological significance. However, the dynamics of social influence in signed networks have nonetheless received fairly little attention. In this article, we propose a generalized Pólya urn model that considers the effect of negative relationships and is capable of comprehending the specific mechanisms of homophily and xenophobia in the dynamics. Based on the mathematical deduction, we find that the signed network guides social influence in a trend toward equality. Simulation shows that a higher effect or larger proportion of negative relationships may break the self-reinforcement and make the market more equal. The collective dynamics in the signed network are more predictable but generate path dependence. We also find that a balanced structure has no impact on the average market share but is helpful in removing path dependence and promoting system stability.

Cooperative evolutionary games have been a focus in various subjects, but the discussion on signed networks is lacking, especially when players are assumed to have different degree of closeness with others, i.e. continuous signed network. To fill this gap, we introduce a negativity coefficient that responds to the effect of negative relationships and a new strategy imitation method that applies to continuous networks. The simulation results show the necessity of negative relations that promotes cooperation, and the increase of the influence of negative relations facilitates the generation of cooperative behaviors. The edge updating in adaptive networks promote cooperation but has little impact when negative edge influence is small. The experiments also prove that structural balance is helpful for increasing the share of cooperators in the evolution.

乡村土地是村民生存的重要资本,土地征收在十四五时期仍将发挥着促进产业转型升级以及推动经济社会快速发展的重要作用,但可能伴随着大量的土地纠纷,引发村民征地集群行为.结合文献计量可视化分析近20 年发表的期刊论文,基于国内征地集群行为的特殊性,梳理西方经典理论和国内研究成果,将征地集群行为的研究聚焦于概念内涵、解释框架、影响因素、演化路径、对策研究五个核心内容.集群行为的发生是以人际互动作为载体,在乡村振兴新时期背景下逐渐呈现出复杂性特征,承载互动关系的社会网络是研究集群行为的关键.目前研究缺乏整体网视角,忽视了乡村社会网络所产生的社会影响.根据目前集群行为研究思维与方法现状,推广以信息传播为基础的复杂网络为代表的交叉科学研究在实践过程中的应用,并探讨在复杂网络视角下征地集群行为的新特征、新影响、新路径与新对策,拓展集群行为领域与复杂网络应用领域的交叉发展.

中国式现代化是人口规模巨大条件下实现全体人民共同富裕的现代化,中国新型城镇化战略推进过程中,中西部地区农业转移人口的家庭功能与可持续发展问题需要重点关注.通过梳理新型城镇化发展的问题,发现新型城镇化面临少子老龄化与性别失衡的人口态势、日趋明显的家庭化迁移趋势、不断弱化的家庭功能、外部风险频发的宏观环境、严重滞后且情况复杂的中西部城镇化发展现状等挑战.将加强以家庭成员就业为代表的经济功能,以婚姻、生育、抚幼和养老为代表的非经济功能作为核心研究问题,提出包括以人为核心的新型城镇化理论与态势研究、农业转移人口家庭经济功能与生计转型、农业转移人口家庭非经济功能与家庭发展、县域新型城镇化进程中的政策响应及成本测算与优化等重点内容在内的多学科交叉研究框架.未来研究新型城镇化应体现微观研究与宏观研究相结合、定性研究与定量研究相结合、静态研究与动态研究相结合、理论研究与政策研究相结合.

Background China is the most populous country globally and has made significant achievements in the control of infectious diseases over the last decades. The 2003 SARS epidemic triggered the initiation of the China Information System for Disease Control and Prevention (CISDCP). Since then, numerous studies have investigated the epidemiological features and trends of individual infectious diseases in China; however, few considered the changing spatiotemporal trends and seasonality of these infectious diseases over time. Objective This study aims to systematically review the spatiotemporal trends and seasonal characteristics of class A and class B notifiable infectious diseases in China during 2005-2020. Methods We extracted the incidence and mortality data of 8 types (27 diseases) of notifiable infectious diseases from the CISDCP. We used the Mann-Kendall and Sen’s methods to investigate the diseases’ temporal trends, Moran I statistic for their geographical distribution, and circular distribution analysis for their seasonality. Results Between January 2005 and December 2020, 51,028,733 incident cases and 261,851 attributable deaths were recorded. Pertussis (P=.03), dengue fever (P=.01), brucellosis (P=.001), scarlet fever (P=.02), AIDS (P<.001), syphilis (P<.001), hepatitis C (P<.001) and hepatitis E (P=.04) exhibited significant upward trends. Furthermore, measles (P<.001), bacillary and amebic dysentery (P<.001), malaria (P=.04), dengue fever (P=.006), brucellosis (P=.03), and tuberculosis (P=.003) exhibited significant seasonal patterns. We observed marked disease burden–related geographic disparities and heterogeneities. Notably, high-risk areas for various infectious diseases have remained relatively unchanged since 2005. In particular, hemorrhagic fever and brucellosis were largely concentrated in Northeast China; neonatal tetanus, typhoid and paratyphoid, Japanese encephalitis, leptospirosis, and AIDS in Southwest China; BAD in North China; schistosomiasis in Central China; anthrax, tuberculosis, and hepatitis A in Northwest China; rabies in South China; and gonorrhea in East China. However, the geographical distribution of syphilis, scarlet fever, and hepatitis E drifted from coastal to inland provinces during 2005-2020. Conclusions The overall infectious disease burden in China is declining; however, hepatitis C and E, bacterial infections, and sexually transmitted infections continue to multiply, many of which have spread from coastal to inland provinces

Human behaviors are often subject to conformity, but little research attention has been paid to social dilemmas in which players are assumed to only pursue the maximization of their payoffs. The present study proposed a generalized prisoner dilemma model in a signed network considering conformity. Simulation shows that conformity helps promote the imitation of cooperative behavior when positive edges dominate the network, while negative edges may impede conformity from fostering cooperation. The logic of homophily and xenophobia allows for the coexistence of cooperators and defectors and guides the evolution toward the equality of the two strategies. We also find that cooperation prevails when individuals have a higher probability of adjusting their relation signs, but conformity may mediate the effect of network adaptation. From a population-wide view, network adaptation and conformity are capable of forming the structures of attractors or repellers.

Finding dense subgraphs is a central problem in graph mining, with a variety of real-world application domains including biological analysis, financial market evaluation, and sociological surveys. While a series of studies have been devoted to finding subgraphs with maximum density, the problem of finding multiple subgraphs that best cover an input network has not been systematically explored. The present study discusses a variant of the densest subgraph problem and presents a mathematical model for optimizing the total coverage of an input network by extracting multiple subgraphs. A memetic algorithm that maximizes coverage is proposed and shown to be both effective and efficient. The method is applied to real-world networks. The empirical meaning of the optimal sampling method is discussed.

The topological characterization of complex systems has significantly contributed to our understanding of the principles of collective dynamics. However, the representation of general complex networks is not enough for explaining certain problems, such as collective actions. Considering the effectiveness of hypernetworks on modeling real-world complex networks, in this paper, we proposed a hypernetwork-based Pólya urn model that considers the effect of group identity. The mathematical deduction and simulation experiments show that social influence provides a strong imitation environment for individuals, which can prevent the dynamics from being self-correcting. Additionally, the unpredictability of the social system increases with growing social influence, and the effect of group identity can moderate market inequality caused by individual preference and social influence. The present work provides a modeling basis for a better understanding of the logic of collective dynamics.

Since the outbreak of the COVID-19 pandemic, Fangcang shelter hospitals have been built and operated in several cities, and have played a huge role in epidemic prevention and control. How to use medical resources effectively in order to maximize epidemic prevention and control is a big challenge that the government should address. In this paper, a two-stage infectious disease model was developed to analyze the role of Fangcang shelter hospitals in epidemic prevention and control, and examine the impact of medical resources allocation on epidemic prevention and control. Our model suggested that the Fangcang shelter hospital could effectively control the rapid spread of the epidemic, and for a very large city with a population of about 10 million and a relative shortage of medical resources, the model predicted that the final number of confirmed cases could be only 3.4% of the total population in the best case scenario. The paper further discusses the optimal solutions regarding medical resource allocation when medical resources are either limited or abundant. The results show that the optimal allocation ratio of resources between designated hospitals and Fangcang shelter hospitals varies with the amount of additional resources. When resources are relatively sufficient, the upper limit of the proportion of makeshift hospitals is about 91%, while the lower limit decreases with the increase in resources. Meanwhile, there is a negative correlation between the intensity of medical work and the proportion of distribution. Our work deepens our understanding of the role of Fangcang shelter hospitals in the pandemic and provides a reference for feasible strategies by which to contain the pandemic.

The community structure in fully signed networks that considers both node attributes and edge signs is important in computational social science; however, its physical description still requires further exploration, and the corresponding measurement remains lacking. In this paper, we present a generalized framework of community structure in fully signed networks, based on which a variant of modularity is designed. An optimization algorithm that maximizes modularity to detect potential communities is also proposed. Experiments show that the proposed method can efficiently optimize the objective function and perform effective community detection.

Network structure plays an important role in the natural and social sciences. Optimization of network structure in achieving specified goals has been a major research focus. In this paper, we focus on structural optimization in terms of minimizing the network’s average path length (APL) by adding edges. We suggest a memetic algorithm to find the minimum-APL solution by adding edges. Experiments show that the proposed algorithm can solve this problem efficiently. Further, we find that APL will ultimately decrease linearly in the process of adding edges, which is affected by the network diameter.