基于度与路径优先连接的集聚型供应链网络演化模型

集聚型供应链供应链网络具有无标度性、高集聚性等特征.以往研究忽视了供应链网络的高集聚性,使得供应链网络模型不能够准确刻画实际的集聚型供应链网络.本文在具体分析集聚型供应链网络动态演化特征的基础上,提出了基于度与路径优先连接的集聚型供应链网络演化模型,弥补了优先连接仅依赖于节点度值的不足.最后,对集聚型供应链网络的度分布、集聚系数和平均最短路径参数进行了数值模拟,模拟结果表明,该模型不仅能够反映集聚型供应链网络的无标度性,而且能够真实刻画其高集聚性特征.     

标度指数不大于2的无标度网络的若干性质

将标度指数不大于2的无标度网络称为亚标度网络.通过引入度秩函数研究了亚标度网络的最大度、平均度以及拓扑结构的非均匀性,通过与标度指数大于2的无标度网络对比,揭示了亚标度网络若干特殊性质.     

复杂网络演化中的“熵减点”研究:以微博传播网络的演化为例

基于标准网络结构熵这一度量"有序性"的指标,以微博传播网络为例,首次将这一宏观指标运用到网络的微观演化研究中.首先,根据"网络大V"参与情况的不同,将微博传播网络分为"无V型网络"、"单V型网络"和"多V型网络".其次,依托实际数据,在标准结构熵的视角下,分别运用枚举法、跟踪法、介入法探讨了这三种网络的演化特征,发现了网络中不同节点的微观演化对宏观"有序性"的差异性影响,其中"大V"节点具备明显的"熵减"效应.最后,归纳提出"熵减点"的相关概念,并提出一套用来识别复杂网络重要节点的"熵减点判别法".方法相较于传统方法具有一些优点且适用性较强,可以扩展到其他复杂系统与复杂网络研究中,为网络结构熵理论的实际应用提供了一些思路.     

BA模型的数学基础——纪念李国平院士吴新谋教授诞辰100周年

该文基于马氏链的概念和技巧, 给出了BA无标度网络模型稳态度分布存在性的严格证明, 并且从数学上重新推导了度分布的精确解析表达式. 此处所用的方法具有一定的普适性, 适用于更一般的无标度型复杂网络模型.     

BA无标度网络中的SIR模型

网络化是现代世界的一个重要特征,不仅包括互联网,还包括航空网,人际关系网,而它们都是很典型的无标度网络.在BA无标度网中,结合经典的SIR模型,建立了新的SIR模型,并对模型进行了研究,得到了BA无标度网络对传染病与计算机病毒传播具有脆弱性的结论.     

利益驱动对创新网络合作行为演化的影响机理及仿真——基于复杂网络拓扑结构视角

随着合作创新的网络化发展,创新网络中各主体的利益诉求对网络演化的影响不断加深。基于不同网络拓扑结构视角下不同利益对创新网络主体合作行为的影响,建立了创新网络演化博弈模型。在无标度与小世界网络拓扑结构下,结合声誉机制、学习行为、网络环境等因素,对创新网络中合作行为的演化进行了仿真研究。结果表明:创新网络的合作行为演化受到网络结构及规模的双重影响,在无标度网络下,合作率更加稳定;合作利益的公平分配促进创新网络合作行为的演化;机会利益的诱惑导致创新网络合作行为的显著波动。     

M-G-P型复杂网络中有寿命节点的进入退出机制

现实中复杂网络结构复杂,形式多样,处在高度动态变化的过程.为了更好地理解真实网络的演化,基于复杂网络的特性进行分析,建立了Poissotn连续时间增长节点具有寿命的M-G-P型复杂网络模型,模型中包括:新节点加入、节点老化和老节点退出等,基于齐次马尔可夫链对模型的度分布进行计算,得出M-G-P型网络的度分布符合幂律分布,模型和BA模型一样能产生指数γ=3的无标度网络,验证了导致无标度网络度分布特征起关键性作用的是链接的偏好特性.     

中国银行间拆借网络结构实证研究

银行间网络结构在银行系统的系统性风险研究中起着十分重要的作用.目前,大多数估算银行间拆借网络结构的研究均采用最大熵估计方法,该方法估算出的网络结构接近全连接的网络结构,从而会低估银行系统的系统性风险.为了估算出更接近中国真实银行系统的银行间拆借网络,文章首次采用最小密度法,用2016年256家中国银行的真实数据,对中国银行间的拆借网络进行了估算.然后从中介中心性和接近中心性以及度的分布等角度研究了中国银行间拆借网络的拓扑特性,并与其他国家的银行间拆借网络结构进行了比较分析.文章通过对实证数据的研究发现:首先中国的银行系统的拆借网络存在分层结构.其次,中国银行系统的银行间拆借网络结构属于无标度网络结构,但不同层中的银行的度的分布呈现不同指数的幂律分布.最后,在描述银行中心性的指标中,中介中心性能更好地描述中国的银行的重要程度,结果与入选"全球系统重要性银行"的中国的银行完全一致.     

群体行为的异质节点复杂网络传播

当遭遇突发的公共社会安全事件时,具有负面影响的应激行为可能迅速在社会范围内传播扩散,形成群体行为.虽然一些复杂网络的传染模型能够对此进行刻画,但更为符合实际的是行为群体根据一些特性可能划分为不同的亚群体,为此将建立异质节点SIS复杂网络模型.此后,依据亚群体的有效传播率与度分布无关、正相关和负相关三种情形,分别研究了群体行为在异质节点的小世界网络传播特性,及异质节点的无标度网络传播特性.无论是异质节点的小世界网络模型还是异质节点的无标度网络模型,平均场动力学分析和计算机模拟结果显示,当亚群体的传播率与度分布呈正相关时,群体行为的传播会出现放大相应;反之,当亚群体的传播率与度分布成负相关时,群体行为的传播会出现抑制效应.但以上的两种效应在离散性更强的无标度网络上更为明显.     

探讨几类具有完全图核心的网络模型的优美性

在复杂网络研究中,人们需要建立网络模型,无标度图就是这样的一种网络模型.我们发现具有完全图核心的网络模型可以演变成无标度图.具有完全图核心的几种网络模型的优美性得到研究.     

An analysis of the structural complexity of supply chain networks

Each enterprise in a supply chain network needs quantitative indicators to analyze and manage its interactions with different business partners in the network. Supply chains exhibit the characteristics of complex systems. In a supply chain network, a large number of firms cooperate simultaneously with many suppliers and customers, and interact through a variety of information and material flows to achieve a balance between supply and demand. However, the complexity of a supply chain is not a simple linear structure where a small change often results in a chain reaction. When supply chain complexity increases, monitoring and managing the interaction between different elements of the chain becomes more difficult. An entropy model based on information theory provides an appropriate means of quantifying the complexity of a supply chain system by delivering information required to describe the state of the system. The entropy measure links uncertainty and complexity so that, as a system grows in uncertainty, it becomes more complex and more information is required to describe and monitor it. In this paper, we propose an entropy-based measure for analyzing the structural complexity in relation to the structure and system uncertainty. The method provides guidelines for estimating the complexity throughout the supply chain structure.     

On the possible values of the entropy of undirected graphs

The entropy of a digraph is a fundamental measure that relates network coding, information theory, and fixed points of finite dynamical systems. In this article, we focus on the entropy of undirected graphs. We prove any bounded interval only contains finitely many possible values of the entropy of an undirected graph. We also determine all the possible values for the entropy of an undirected graph up to the value of four.     

A methodology for analyzing decision networks, based on information theory

This paper assumes the organization as a distributed decision network. It proposes an approach based on application and extension of information theory concepts, in order to analyze informational complexity in a decision network, due to interdependence between decision centers.Based on this approach, new quantitative concepts and definitions are proposed in order to measure the information in a decision center, based on Shannon entropy and its complement in possibility theory, U uncertainty. This approach also measures the quantity of interdependence between decision centers and informational complexity of decision networks.The paper presents an agent-based model of organization as a graph composed of decision centers. The application of the proposed approach is in analyzing and assessing a measure to the organization structure efficiency, based on informational communication view. The structure improvement, analysis of information flow in organization and grouping algorithms are investigated in this paper. The results obtained from this model in different systems as distributed decision networks, clarifies the importance of structure and information distribution sources effect’s on network efficiency.     

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean–variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.     

Symmetry in complex networks

We consider the size and structure of the automorphism groups of a variety of empirical ‘real-world’ networks and find that, in contrast to classical random graph models, many real-world networks are richly symmetric. We construct a practical network automorphism group decomposition, relate automorphism group structure to network topology and discuss generic forms of symmetry and their origin in real-world networks. We also comment on how symmetry can affect network redundancy and robustness.     

创新网络合作密度影响因素及其仿真分析

合作创新模式使得创新主体之间构成具有拓扑结构特性的创新网络,创新主体合作行为会影响创新网络的可持续发展。为探究不同影响因素作用下创新网络合作密度的变化,基于演化博弈理论和BA无标度网络理论,运用仿真方法,分析了网络规模、聚类系数、偏好性仿真、利益分配对创新网络合作密度的影响。结果表明:小规模的创新网络具有较低的聚类系数有利于网络合作密度的提升,大规模的创新网络具有较高的聚类系数有利于网络合作密度的提升;无论是大规模还是小规模创新网络,适度的偏好性模仿均能促进网络合作密度的提升;对于利益分配而言,无论是大规模还是小规模的创新网络,按劳分配是提升网络合作密度的最佳利益分配形式。     

USING NETWORK ANALYSIS TO CHARACTERIZE FOREST STRUCTURE

Abstract Network analysis quantifies different structural properties of systems of interrelated parts using a single analytical framework. Many ecological phenomena have network‐like properties, such as the trophic relationships of food webs, geographic structure of metapopulations, and species interactions in communities. Therefore, our ability to understand and manage such systems may benefit from the use of network‐analysis techniques. But network analysis has not been applied extensively to ecological problems, and its suitability for ecological studies is uncertain. Here, we investigate the ability of network analysis to detect spatial patterns of species association in a tropical forest. We use three common graph‐theoretic measures of network structure to quantify the effect of understory tree size on the spatial association of understory species with trees in the canopy: the node degree distribution (NDD), characteristic path length (CPL), and clustering coefficient (CC). We compute the NDD, CPL, and CC for each of seven size classes of understory trees. For significance testing, we compare the observed values to frequency distributions of each statistic computed from randomized data. We find that the ability of network analysis to distinguish observed patterns from those representing randomized data strongly depends on which aspects of structure are investigated. Analysis of NDD finds no significant difference between random and observed networks. However, analysis of CPL and CC detected nonrandom patterns in three and one of the seven size classes, respectively. Network analysis is a very flexible approach that holds promise for ecological studies, but more research is needed to better understand its advantages and limitations.     

基于模糊证据推理的汽车制造产业网络环境复杂性测度

环境复杂性是汽车制造产业网络复杂性的主要诱因。利用指标波动程度测度复杂性的思想,建立表征汽车制造产业网络外部环境的指标体系,构建指标变动程度的模糊信度结构模型,运用模糊证据推理算法结合熵值理论对多指标进行信息融合,并利用灰色关联分析方法评估复杂性测度结果。通过2002~2012年汽车行业数据的实证研究,结果表明:经济环境与国际环境的波动是导致汽车制造产业网络复杂性的最主要因素,技术环境和市场环境次之;近10年来我国汽车制造产业网络外部环境经历了两次较大波动,总体复杂性呈现增长趋势。本研究可为汽车产业环境复杂性测度及应对提供思路参考。     

Network formation and knowledge gains

Incentive-based models for network formation link micro actions to changes in network structure. Sociologists have extended these models on a number of fronts, but there remains a tendency to treat actors as homogenous agents and to disregard social theory. Drawing upon literature on the strategic use of networks for knowledge gains, we specify models exploring the co-evolution of networks and knowledge gains. Our findings suggest that pursuing transitive ties is the most successful strategy, as more reciprocity and cycling result from this pursuit, thus encouraging learning across the network. We also discuss the role of network size, global network structure, and parameter strength in actors’ attainment of knowledge resources.     

用Hopfield网络计算约束条件下系统熵的最小值

有约束条件时系统熵的最小值问题是ＮＰ 完全问题，该文利用Ｈｏｐｆｉｅｌｄ人工神经网络解决组合优化问题的能力计算了此问题，得到了较好的结果．