漫谈社会物理学

进入21世纪以来,社会系统的复杂性已经逐步成为科学研究的热点领域,社会物理学利用物理学的思想和方法研究社会问题,已经取得了不少成果.文章首先简要回顾了社会物理学发展的历史,然后简要介绍了当代社会物理学的研究问题和成果,主要包括行人动力学、社会网络分析和舆论动力学三个方面.涉及社会力作用下行人的运动以及所表现出的群体行为、社会网络结构分析的基本概念,特别是社团结构的定义及其探测方法、基于自旋相互作用的舆论形成模型和相变行为等.     

Discrete Opinion Dynamics on Online Social Networks

This paper focuses on the dynamics of binary opinions {+1,-1} on online social networks consisting of heterogeneous actors.In our model,actors update their opinions under the interplay of social influence and selfaffirmation,which leads to rich dynamical behaviors on online social networks.We find that the opinion leading to the consensus features an advantage of the initially weighted fraction based on actors' strength over the other,instead of the population.For the role of specific actors,the consensus converges towards the opinion that a small fraction of high-strength actors hold,and individual diversity of self-affirmation slows down the ordering process of consensus.These indicate that high-strength actors play an essential role in opinion formation with strong social influence as well as high persistence.Further investigations show that the initial fraction of high-strength actors to dominate the evolution depends on the heterogeneity of the strength distribution,and less high-strength actors are needed in the case of a smaller exponent of power-law distribution of actors' strength.Our study provides deep insights into the role of social influence and self-affirmation on opinion formation on online social networks.     

Constant evacuation time gap: Experimental study and modeling

In this paper, the evacuation dynamics in an artificial room with only one exit is investigated via experiments and modeling. Two sets of experiments are implemented, in which pedestrians are asked to escape individually. It is found that the average evacuation time gap is essentially constant. To model the evacuation dynamics, an improved social force model is proposed, in which it is assumed that the driving force of a pedestrian cannot be performed when the resultant physical force exceeds a threshold. Simulation results are in good agreement with the experimental ones.     

A Class of Rumor Spreading Models with Population Dynamics

Based on the characteristics of rumor spreading in online social networks, this paper proposes a new rumor spreading model. This is an improved SIS rumor spreading model in online social networks that combines the transmission dynamics and population dynamics with consideration of the impact of both of the changing number of online social network users and different levels of user activity. We numerically simulate the rumor spreading process. The results of numerical simulation show that the improved SIS model can successfully characterize the rumor spreading behavior in online social networks. We also give the effective strategies of curbing the rumor spreading in online social networks.     

Theory of rumour spreading in complex social networks

We introduce a general stochastic model for the spread of rumours, and derive mean-field equations that describe the dynamics of the model on complex social networks (in particular, those mediated by the Internet). We use analytical and numerical solutions of these equations to examine the threshold behaviour and dynamics of the model on several models of such networks: random graphs, uncorrelated scale-free networks and scale-free networks with assortative degree correlations. We show that in both homogeneous networks and random graphs the model exhibits a critical threshold in the rumour spreading rate below which a rumour cannot propagate in the system. In the case of scale-free networks, on the other hand, this threshold becomes vanishingly small in the limit of infinite system size. We find that the initial rate at which a rumour spreads is much higher in scale-free networks than in random graphs, and that the rate at which the spreading proceeds on scale-free networks is further increased when assortative degree correlations are introduced. The impact of degree correlations on the final fraction of nodes that ever hears a rumour, however, depends on the interplay between network topology and the rumour spreading rate. Our results show that scale-free social networks are prone to the spreading of rumours, just as they are to the spreading of infections. They are relevant to the spreading dynamics of chain emails, viral advertising and large-scale information dissemination algorithms on the Internet.     

Structure and stability of online chat networks built on emotion-carrying links

High-resolution data of online chats are studied as a physical system in the laboratory in order to quantify collective behavior of users. Our analysis reveals strong regularities characteristic of natural systems with additional features. In particular, we find self-organized dynamics with long-range correlations in user actions and persistent associations among users that have the properties of a social network. Furthermore, the evolution of the graph and its architecture with specific k$k$-core structure are shown to be related with the type and the emotion arousal of exchanged messages. Partitioning of the graph by deletion of the links which carry high arousal messages exhibits critical fluctuations at the percolation threshold.     

基于信息熵的社交网络观点演化模型

随着网络服务的发展,社交网络逐渐成为信息传播的新媒介.因此,研究网络舆情演化具有重要意义和实用价值.为了更好地研究网络舆论,在信息熵的基础上,提出了一个社交网络观点演化模型.此模型存在以下两个特点:一是可以反映个体面对正负两种观点趋向做出抉择时的心理过程;二是可以反映个体形成新观点时主观因素和客观因素的影响.在仿真实验中,讨论了舆论环境对个体观点演化的影响,初始观点和自信度对观点演化的影响,以及意见领袖对群体观点演化的影响.实验结果表明,该模型可以反映真实社交网络中个体的心理学特征,比如个体的观点形成会受到舆论环境的影响,自信的个体不愿意接受他人的观点,当意见领袖存在时群体的观点会受到影响等.     

Opinion Evolution in Divided Community

Our agent-based model of opinion dynamics concerns the current vast divisions in modern societies. It examines the process of social polarization, understood here as the partition of a community into two opposing groups with contradictory opinions. Our goal is to measure how mutual animosities between parties may lead to their radicalization. We apply a double-clique topology with both positive and negative ties to the model of binary opinions. Individuals are subject to social pressure; they conform to the opinions of their own clique (positive links) and oppose those from the other one (negative links). There is also a chance of acting independently, which alters the system’s behavior in various ways, depending on its magnitude. The results, obtained with both Monte-Carlo simulations and the mean-field approach, lead to two main conclusions: in such a system, there exists a critical quantity of negative relations that are needed for polarization to occur, and (rather surprisingly) independent actions actually support the process, unless their frequency is too high, in which case the system falls into total disorder.     

复杂网络上的意见动力学对谣言传播的影响

通过在SIR(susceptible-infected-recovered)模型中引入抑制者对谣言的辟谣机制研究了在线社交网络上的意见动力学对谣言传播的影响.在这一模型中,节点可以与自身的邻居组成1个群,传播者可以通过该群传播信息,抑制者也可以在此群中对信息发表意见进行辟谣.辟谣机制在降低未知者对于谣言的接受概率的同时也可以促使传播者向抑制者转变.本文采用ER(Erd?s-Rényi)随机网络、无标度网络以及真实的社交网络研究了抑制者的沉默概率对于谣言传播范围的影响.首先发现,谣言传播的过程以传播者的峰值为界可以分为两个阶段,即谣言自由传播的前期以及抑制者和传播者互相制衡的后期;其次,谣言的传播会随着抑制者的沉默概率的增大而突然暴发.在谣言暴发阈值之下,沉默概率的增大不会导致谣言传播范围显著增大,这是由于未知者在感知到谣言并转变为传播者后又迅速转变为抑制者;而当沉默概率达到谣言暴发阈值时,抑制者将不能控制传播者对谣言的传播从而导致抑制者的降低和谣言的暴发;最后,无标度上的谣言自由传播的前期阶段比随机网络持续的时间更短,从而使无标度上的谣言更难以暴发.本文的模型综合考虑了意见动力学和谣言传播的相互作用,更加真实地模拟了真实世界社交网络中的谣言传播过程.为谣言传播的控制和干预提供了一些有用的思路和见解.     

Coupled disease–behavior dynamics on complex networks: A review

It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease–behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease–behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.     

基于在线社交网络的信息传播模型

本文构造了一个基于在线社交网络的信息传播模型.该模型考虑了节点度和传播机理的影响,结合复杂网络和传染病动力学理论,进一步建立了动力学演化方程组.该方程组刻画了不同类型节点随着时间的演化关系,反映了传播动力学过程受到网络拓扑结构和传播机理的影响.本文模拟了在线社交网络中的信息传播过程,并分析了不同类型节点在网络中的行为规律.仿真结果表明:由于在线社交网络的高度连通性,信息在网络中传播的门槛几乎为零;初始传播节点的度越大,信息越容易在网络中迅速传播;中心节点具有较大的社会影响力;具有不同度数的节点在网络中的变 关键词： 在线社交网络 信息传播 微分方程 传染病动力学     

Hybrid phase transitions of spreading dynamics in multiplex networks

In this paper, we study the spreading dynamics of social behaviors and focus on heterogenous responses of individuals depending on whether they realize the spreading or not. We model the system with a two-layer multiplex network, in which one layer describes the spreading of social behaviors and the other layer describes the diffusion of the awareness about the spreading. We use the susceptible-infected-susceptible (SIS) model to describe the dynamics of an individual if it is unaware of the spreading of the behavior. While when an individual is aware of the spreading of the social behavior its dynamics will follow the threshold model, in which an individual will adopt a behavior only when the fraction of its neighbors who have adopted the behavior is above a certain threshold. We find that such heterogenous reactions can induce intriguing dynamical properties. The dynamics of the whole network may exhibit hybrid phase transitions with the coexistence of continuous phase transition and bi-stable states. Detailed study of how the diffusion of the awareness influences the spreading dynamics of social behavior is provided. The results are supported by theoretical analysis.     

The effects of nonlinear interactions and network structure in small group opinion dynamics

We present a model of opinion dynamics in social networks in which an individual's opinion evolves under the action of (i) a linear force which tends to restore the opinion back towards the individual's natural bias that is his or her initial opinion and (ii) a nonlinear coupling with other individuals which acts to bring opinions closer together but wanes for high opinion discrepancies. Bifurcation analysis for the case of a two-person group shows that a critical value for the difference in natural biases exists which demarcates regimes of qualitatively different behavior. For low to moderate natural bias differences, the dynamics are qualitatively similar to linear theory. For high bias differences, the system takes on a binary nature and is marked by discontinuous transitions between deadlock and consensus as well as hysteresis as the coupling is varied. The coupling required to force consensus grows extremely rapidly with the natural bias difference indicating that trying to achieve group consensus solely via increasing the communications rate becomes fruitless as the biases become extremely divergent. We also show that, for high bias differences, a triad broker network topology can reduce group discord more effectively than a clique, contrary to linear theory.     

How people make friends in social networking sites—A microscopic perspective

We study the detailed growth of a social networking site with full temporal information by examining the creation process of each friendship relation that can collectively lead to the macroscopic properties of the network. We first study the reciprocal behavior of users, and find that link requests are quickly responded to and that the distribution of reciprocation intervals decays in an exponential form. The degrees of inviters/accepters are slightly negatively correlative with reciprocation time. In addition, the temporal feature of the online community shows that the distributions of intervals of user behaviors, such as sending or accepting link requests, follow a power law with a universal exponent, and peaks emerge for intervals of an integral day. We finally study the preferential selection and linking phenomena of the social networking site and find that, for the former, a linear preference holds for preferential sending and reception, and for the latter, a linear preference also holds for preferential acceptance, creation, and attachment. Based on the linearly preferential linking, we put forward an analyzable network model which can reproduce the degree distribution of the network. The research framework presented in the paper could provide a potential insight into how the micro-motives of users lead to the global structure of online social networks.     

随机中子动力学及其数值模拟研究进展

随机中子动力学是核动力设计和核反应堆安全中的重要课题,本文从随机中子动力学的基础概念和研究方法出发,介绍随机中子动力学研究的历史发展和研究现状.裂变中子与光子的多重性是反应堆零功率中子噪声主要来源,对中子涨落的方程描述及其求解,演化出零功率中子噪声与功率反应堆噪声的随机理论.随机中子动力学的重要应用包括反应性微观测量、功率反应堆噪声测量和分析、核临界漂移分析和核材料识别与检测等.在半个多世纪的研究中,以脉冲堆点火过程的脉冲爆发等待时间分布为代表的随机性,一直缺乏定量分析方法和工具.直到近几年,模拟随机中子动力学过程的广义半马尔科夫过程模拟方法取得了重要进展,很好地揭示了脉冲堆实验中子点火规律.最后讨论随机中子动力学研究中有待解决的研究课题.     

Trench formation and lateral damage induced by gallium milling of silicon

Molecular dynamics simulations are performed to model the nanomachining of materials via focused ion beams (FIBs). The goal of this research is to investigate the fundamental dynamics which govern the interaction of FIB with materials which are vital to the semiconductor industry, namely silicon. Specifically, we focus on the formation of trenches/holes within the sample and the dynamics responsible for their characteristic v-shape, as well as the extent of lateral damage due to a gallium beam. These phenomena have been successfully modelled, with evidence that the lateral and subsurface damage created is much larger than the beam itself. The results presented here begin to elucidate the dynamics governing the spatial resolution of these experiments, and provide an idea of some of the technical issues associated with these beams.     

Disease dynamics in a dynamic social network

We develop a framework for simulating a realistic, evolving social network (a city) into which a disease is introduced. We compare our results to prevaccine era measles data for England and Wales, and find that they capture the quantitative and qualitative features of epidemics in populations spanning two orders of magnitude. Our results provide unique insight into how and why the social topology of the contact network influences the propagation of the disease through the population. We argue that network simulation is suitable for concurrently probing contact network dynamics and disease dynamics in ways that prior modeling approaches cannot and it can be extended to the study of less well-documented diseases.     

Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction

Finding the building blocks of real-world networks contributes to the understanding of their formation process and related dynamical processes, which is related to prediction and control tasks. We explore different types of social networks, demonstrating high structural variability, and aim to extract and see their minimal building blocks, which are able to reproduce supergraph structural and dynamical properties, so as to be appropriate for diffusion prediction for the whole graph on the base of its small subgraph. For this purpose, we determine topological and functional formal criteria and explore sampling techniques. Using the method that provides the best correspondence to both criteria, we explore the building blocks of interest networks. The best sampling method allows one to extract subgraphs of optimal 30 nodes, which reproduce path lengths, clustering, and degree particularities of an initial graph. The extracted subgraphs are different for the considered interest networks, and provide interesting material for the global dynamics exploration on the mesoscale base.     

Epistemic Communities under Active Inference

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.     

Dynamics of bloggers’ communities: Bipartite networks from empirical data and agent-based modeling

We present an analysis of the empirical data and the agent-based modeling of the emotional behavior of users on the Web portals where the user interaction is mediated by posted comments, like Blogs and Diggs. We consider the dataset of discussion-driven popular Diggs, in which all comments are screened by machine-learning emotion detection in the text, to determine positive and negative valence (attractiveness and aversiveness) of each comment. By mapping the data onto a suitable bipartite network, we perform an analysis of the network topology and the related time-series of the emotional comments. The agent-based model is then introduced to simulate the dynamics and to capture the emergence of the emotional behaviors and communities. The agents are linked to posts on a bipartite network, whose structure evolves through their actions on the posts. The emotional states (arousal and valence) of each agent fluctuate in time, subject to the current contents of the posts to which the agent is exposed. By an agent’s action on a post its current emotions are transferred to the post. The model rules and the key parameters are inferred from the considered empirical data to ensure their realistic values and mutual consistency. The model assumes that the emotional arousal over posts drives the agent’s action. The simulations are preformed for the case of constant flux of agents and the results are analyzed in full analogy with the empirical data. The main conclusions are that the emotion-driven dynamics leads to long-range temporal correlations and emergent networks with community structure, that are comparable with the ones in the empirical system of popular posts. In view of pure emotion-driven agents actions, this type of comparisons provide a quantitative measure for the role of emotions in the dynamics on real blogs. Furthermore, the model reveals the underlying mechanisms which relate the post popularity with the emotion dynamics and the prevalence of negative emotions (critique). We also demonstrate how the community structure is tuned by varying a relevant parameter in the model. All data used in these works are fully anonymized.