A new local-world evolving network model

In some real complex networks, only a few nodes can obtain the global information about the entire networks, but most of the nodes own only local connections therefore own only local information of the networks. A new local-world evolving network model is proposed in this paper. In the model, not all the nodes obtain local network information, which is different from the local world network model proposed by Li and Chen （LC model）. In the LC model, each node has only the local connections therefore owns only local information about the entire networks. Theoretical analysis and numerical simulation show that adjusting the ratio of the number of nodes obtaining the global information of the network to the total number of nodes can effectively control the valuing range for the power-law exponent of the new network. Therefore, if the topological structure of a complex network, especially its exponent of power-law degree distribution, needs controlling, we just add or take away a few nodes which own the global information of the network.

The Evolving Bipartite Network and Semi-Bipartite Network Models with Adjustable Scale and Hybrid Attachment Mechanisms

The bipartite graph structure exists in the connections of many objects in the real world, and the evolving modeling is a good method to describe and understand the generation and evolution within various real complex networks. Previous bipartite models were proposed to mostly explain the principle of attachments, and ignored the diverse growth speed of nodes of sets in different bipartite networks. In this paper, we propose an evolving bipartite network model with adjustable node scale and hybrid attachment mechanisms, which uses different probability parameters to control the scale of two disjoint sets of nodes and the preference strength of hybrid attachment respectively. The results show that the degree distribution of single set in the proposed model follows a shifted power-law distribution when parameter r and s are not equal to 0, or exponential distribution when r or s is equal to 0. Furthermore, we extend the previous model to a semi-bipartite network model, which embeds more user association information into the internal network, so that the model is capable of carrying and revealing more deep information of each user in the network. The simulation results of two models are in good agreement with the empirical data, which verifies that the models have a good performance on real networks from the perspective of degree distribution. We believe these two models are valuable for an explanation of the origin and growth of bipartite systems that truly exist.

A local-world evolving hypernetwork model

Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mecha- nisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the hyperedge growth and local-world hyperedge preferential attachment mechanisms. At each time step, a newly added hyperedge encircles a new coming node and a number of nodes from a randomly selected local world. The number of the selected nodes from the local world obeys the uniform distribution and its mean value is m. The analytical and simulation results show that the hyperdegree approximately obeys the power-law form and the exponent of hyperdegree distribution is 7 = 2 ＋ 1/m. Furthermore, we numerically investigate the node degree, hyperedge degree, clustering coefficient, as well as the average distance, and find that the hypemetwork model shares the scale-flee and small-world properties, which shed some light for deeply understanding the evolution mechanism of the real systems.

Evolving Network Model with Local-Area Preference for Mobile Ad Hoc Network

To accurately describe the evolving features of Mobile Ad Hoc Networks (MANETs) and to improve the performance of such networks, an evolving topology model with local-area preference is proposed. The aim of the model, which is analyzed by the mean field theory, is to optimize network structures based on users' behaviors in MANETs. The analysis results indicate that the network generated by this evolving model is a kind of scale-free network. This evolving model can improve the fault-tolerance performance of networks by balancing the connectivity and two factors, i.e., the remaining energy and the distance to nodes. The simulation results show that the evolving topology model has superior performance in reducing the traffic load and the energy consumption, prolonging network lifetime and improving the scalability of networks. It is an available approach for establishing and analyzing actual MANETs.

A Collaboration Network Model with Multiple Evolving Factors

To describe the empirical data of collaboration networks, several evolving mechanisms have been proposed, which usually introduce different dynamics factors controlling the network growth. These models can reasonably reproduce the empirical degree distributions for a number of we11-studied real-world collaboration networks. On the basis of the previous studies, in this work we propose a collaboration network model in which the network growth is simultaneously controlled by three factors, including partial preferential attachment, partial random attachment and network growth speed. By using a rate equation method, we obtain an analytical formula for the act degree distribution. We discuss the dependence of the act degree distribution on these different dynamics factors. By fitting to the empirical data of two typical collaboration networks, we can extract the respective contributions of these dynamics factors to the evolution of each networks.

Evolving networks: from topology to dynamics

A multi-local-world model is introduced to describe the evolving networks that have a localization property such as the Internet. Based on this model, we show that the traffic load defined by 'betweenness centrality' on the multi-local-world scale-free networks' model also follows a power law form. In this kind of network, a few vertices have heavier loads and so play more important roles than the others in the network.

Application of Design Patterns in Process of Large-Scale Software Evolving

To search for the Design Patterns’ influence on the software, the paper abstracts the feature models of 9 kinds of classic exiting design patterns among the 23 kinds and describes the features with algorithm language. Meanwhile, searching for the specific structure features in the network, the paper designs 9 matching algorithms of the 9 kinds design patterns mentioned above to research on the structure of the design patterns in the software network. At last, the paper analyzes the evolving trends of the software scale and the application frequency of the 9 kinds of design patterns as the software evolves, and search for the rules how these design patterns are applied into 4 kinds of typical software.

Epidemic spreading behavior with time delay on local-world evolving networks

An improved susceptible-infected-susceptible(SIS)model in the local-world evolving network model is presented to study the epidemic spreading behavior with time delay,which is added into the infected phase.The local-world evolving model displays a transition from the exponential network to the scale-free network with respect to the degree distribution.Two typical delay regimes,i.e.,uniform and degree-dependent delays are incorporated into the SIS epidemic model to investigate the epidemic infection processes in the local-world net-work model.The results indicate that the infection delay will promote the epidemic outbreaks,increase the prevalence and reduce the critical threshold of epidemic spreading.It is also found that local-world size M will considerably influence the epidemic spreading behavior with time delay in the local-world network through large-scale numerical simulations.Simulation results are also of relevance to fight epidemic outbreaks.

考虑城镇生长特性的区域能源网演化模型

区域能源网的发展是新型城镇建设的重要环节,分析区域能源网的演化规律和形态特征,对于指导网络规划、运行具有重要意义。提出了一种考虑城镇生长特性的区域能源网演化模型,首先根据城镇生长特性总结了空间负荷的增长特性,进而借鉴复杂网络理论中的随机几何图模型模拟城镇负荷的增长过程,然后以最小化投资建设成本为目标,规划得到区域能源网的能源站和综合管线分布,给出区域能源网的演化流程,进一步通过理论分析了演化模型的空间和拓扑特征。算例分析结果表明演化所得区域能源网符合城镇和负荷的生长特性,其形态特征与理论分析结果一致,并且与实际电网具有相似的特征,验证了所提演化模型的合理性。

Evolution model for scientific collaboration network with local-world information

In order to reveal the intrinsic properties of scientific collaboration networks, a new local-world evolution model on a scientific collaboration network is proposed by analysing the network growth mechanism. The act degree as the measurement of preferential attachment is taken, and the local-world information of nodes is taken into account. Analysis and simulation show that the node degree and the node strength obey the power-law distribution. Low average path length and high clustering coefficient are approved. Experiment indicates that the model can depict efficiently the topological structure and statistical characteristics of real-life scientific collaboration networks.

企业创新网络的自组织演化模型

基于企业在创新合作活动中的理性决策行为,建立创新网络动态演化模型。运用复杂网络研究中的数值仿真方法,研究创新网络的动态演化规律、企业间的知识溢出与网络结构的关系。研究放宽了创新网络博弈论文献对网络结构的不合理假定,同时又赋予网络演化一定的经济含义。研究发现:创新网络可自组织演化至稳定状态;知识溢出效率的提高可增进行业的收益水平,并激励企业积极地参与创新合作,但过高的效率值会加剧企业间收益分配的不平等;知识溢出效率不影响企业合作规模的分布;稳定状态下的创新网络为小世界网络,具有高的聚集系数和短的平均路径长度,但其并非利用效率最高的网络安排。

面向北极航线通航的海运网络演化研究

基于复杂网络理论,构建以港口间海运距离和吸引度为基础的复杂网络演化模型。选取全球25个主要集装箱港口2010年相关数据,对比演化网络、实际网络和同等规模的随机网络,验证了模型的有效性。将模型应用于北极航线通航后的海运网络,结果显示,北极航线通航后海运网络平均路径长度减小,表明北极航线开通加强了港口间的联系;部分东北亚及西北欧港口度值增加较为明显;港口核度更趋于极化,表明北极航线增强了海运网络的层次性。

局部拓扑信息耦合促进网络演化

为了研究局部拓扑信息耦合对网络演化的促进作用,该文提出一种局部拓扑加权方法,用于表征节点间联系的紧密性及拓扑信息的耦合程度,并从演化模型的宏观统计和实际网络数据测试两方面验证了局部拓扑信息耦合促进网络演化的有效性。首先将该加权方法应用于BA模型,提出Tw BA模型及局域世界模型Tw LW。仿真实验表明,Tw BA的度分布随连边数目的增多,迅速从指数分布转变为幂律分布,验证了现实网络加速增长产生幂律分布的现象,并基于此提出一种加速演化的Tw BA模型,其在不同的加速率下呈现出幂律分布;而Tw LW则展现了从广延指数分布到幂律分布变化的形式。然后将加权方法拓展到链路预测方法,提出3个加权相似性指标。实际网络数据测试表明,该方法能够大幅度地提高基本算法的预测精度,部分甚至高于全局性指标。

考虑网络演化的直流潮流停电模型与自组织临界性分析

直流潮流停电(OPA)模型及其改进模型能模拟连锁故障停电过程,并能从宏观上揭示大停电的自组织临界性,但没有考虑电网结构变化对大停电的影响。文中在OPA模型基础上,考虑了慢动态过程中电网结构的演化特征,建立了考虑网络演化的OPA模型。对2个典型复杂网络演化方式的仿真表明:按小世界方式和无标度方式演化的电网都会自组织到临界状态;小世界电网特有的高聚类系数和低特征路径长度易扩大连锁故障的广度和深度,其平均损失负荷大于无标度电网;无标度电网通过小规模的线路连锁开断,释放系统压力,减少大停电概率。研究结果对未来的电网建设及大停电的整体预防有一定指导意义。

基于改进BA模型的作者合作加权网络动态演化研究

在考虑科研贡献度及BA模型网络特征的基础上,引入混合度择优连接规则、反择优衰退规则、分段随机概率连接规则,构建了解析网络演化机理的作者合作加权网络演化模型,利用平均场理论和连续介质理论对该网络演化模型的度分布和幂律指数等网络特征进行推导,并运用Matlab对不同初始条件下的网络演化过程进行仿真,结果表明作者合作加权网络的度分布具有"均匀与幂律双重混合分布"和"分段均匀分布"的特点,网络演化过程存在"路径依赖",但是"不敏感依赖于网络初始形态"。同时以CNKI所收录的2009-2014年情报学领域CSSCI来源期刊的合作发文情况对作者合作加权网络演化过程中的网络度分布进行了验证,证实作者合作加权网络动态演化模型具有普适性和合理性。

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。

基于复杂网络理论的装备保障网络模型研究

从网络科学的观点出发,对装备保障网络生成机制进行研究,针对其严格的等级结构和一定的随机连接特性,在对树状网络和确定性复杂网络模型研究的基础上,提出了一个装备保障网络演化模型.该模型考虑了网络中节点和边的实际物理属性,引入了随机连接参数,实现了混合生成的装备保障网络演化机制,并仿真分析了模型中迭代步数、子节点数目、随机连接因子这3个参数对装备保障网络整体性能的影响规律,验证了该模型的有效性和适用性.

一种考虑用户兴趣转移特征的协同预测模型

大多数预测模型使用用户属性或社交关系信息来优化预测结果,然而真实系统中用户的属性或社交关系信息往往很难获得,或者取得的是虚假信息,从而导致用户行为表达不准确或模型不具有普适性.另外,几乎所有使用用户特征的模型仅考虑用户兴趣本身的度量,而忽视兴趣的变化这一重要特征.因此,本文提出一种考虑用户兴趣转移特征的协同预测模型.该模型根据用户连续行为序列构建用户兴趣转移特征和用户行为演变网络,利用用户兴趣转移特征计算用户相似性,进而搜索最近邻集合,利用用户行为演变网络筛选候选集,最后设计最频繁项提取算法来产生预测结果,从而构建用户行为的预测模型.在真实的新闻浏览日志、交互式网络电视视频访问日志和微软服务器日志上的实验表明该预测模型是有效的.

三代电网演化模型及特性分析

三代电网理论将电网发展历程可归纳为三个阶段——第一代小型电网、第二代大型互联电网和第三代智能电网。根据该理论文中分析了三代电网发展历程,从物理层面出发,归纳总结驱动和制约电网演化生长的主要因素,在此基础上提出一种电网演化生长模型,利用该模型成功复现了三代电网演化生长过程,初步奠定了三代电网理论的数学基础。进一步,在长时间尺度上对三代电网演化结果多项网络结构统计特性指标进行计算分析,分析结果从复杂网络层面验证了三代电网理论的正确性和前瞻性。