简易广义合作网络度分布的稳定性

本文对简易广义合作网络的三类特殊情形（择优连接、随机连接、混合连接）进行了研究. 基于马氏链理论, 给出它们度分布稳定性存在的严格证明, 并且得到相应网络度分布和度指数的精确表达式. 特别地, 对于混合连接情况, 说明在连线方式中只要存在择优成分, 网络度分布就服从幂律分布, 即所得网络为无标度网络. 关键词： 简易广义合作网络 无标度网络 马氏链 度分布     

考虑潜伏期的关联信用风险传染机理研究

关联信用风险及其传染是现代信用风险管理的热点和难点问题。本文基于复杂网络的平均场理论,运用传染病模型,来刻画关联主体网络中关联信用风险的传染机理;并引入潜伏主体及其潜伏期,分析了网络中关联信用风险的传染过程;进一步,在关联主体BA无标度网络中,探讨了关联信用风险特征对传染过程中稳定状态的影响。研究结果表明:潜伏主体的平均潜伏期、感染主体的传染率和恢复率及传染力、信用主体的新生率和死亡率,均会影响关联信用风险传染阈值和稳定状态时感染主体密度。     

考虑免疫性的关联信用风险的传染机理研究

现实企业之间广泛的关联关系导致了复杂的关联信用风险传染。本文改进了传染病模型以用于刻画企业之间关联信用风险的传染机制;并进一步,在部分企业可能形成“免疫”能力的背景下,探讨了关联信用风险传染的稳定状态;最后,在关联企业形成无标度网络环境下,分析了关联信用风险特点对该状态的影响。结果表明: 关联信用风险传染阈值和稳定状态感染企业的密度,均与网络初始状态的免疫性企业的比例、企业免疫性的丧失率及救助时间有关。     

Weighted Scaling in Non-growth Random Networks

We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in nongrowth random networks.In this model,we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it.The network evolves according to a vertex strength preferential selection mechanism.During the evolution process,the network always holds its total number of vertices and its total number of single-edges constantly.We show analytically and numerically that a network will form steady scale-free distributions with our model.The results show that a weighted non-growth random network can evolve into scale-free state.It is interesting that the network also obtains the character of an exponential edge weight distribution.Namely,coexistence of scale-free distribution and exponential distribution emerges.     

Degree distribution of a scale-free random graph model

In this paper, we consider the degree distribution of a general random graph with multiple edges and loops from the perspective of probability. Based on the first-passage probability of Markov chains, we give a new and rigorous proof to the existence of the network degree distribution and obtain the precise expression of the degree distribution. The analytical results are in good agreement with numerical simulations.     

Markov chain-based degree distributions of evolving networks

In this paper, we study a class of stochastic processes, called evolving network Markov chains, in evolving networks. Our approach is to transform the degree distribution problem of an evolving network to a corresponding problem of evolving network Markov chains. We investigate the evolving network Markov chains, thereby obtaining some exact formulas as well as a precise criterion for determining whether the steady degree distribution of the evolving network is a power-law or not. With this new method, we finally obtain a rigorous, exact and unified solution of the steady degree distribution of the evolving network.     

Poisson增长竞争网络与适应度模型

提出吸引度依赖于时间的竞争网络模型.利用Poisson过程获得这个模型稳态平均度分布的解析表达式.理论分析表明,这类网络幂律指数与渐近吸引系数和新节点边数m有关,且在区间(1+1/m,m+1)内.作为竞争网络模型的应用,获得了适应度模型的度分布估计.结果表明适应度模型是竞争网络模型的特例,反之则不然.     

增长网络的形成机理和度分布计算

关于增长网络的形成机理,着重介绍由线性增长与择优连接组成的BA模型, 以及加速增长模型.此外,我们提出了一个含反择优概率删除旧连线的模型,这个模型能自组织演化成scale-free(SF)网络.关于计算SF网络的度分布,简要介绍文献上常用的基于连续性理论的动力学方法(包括平均场和率方程)和基于概率理论的主方程方法.另外,我们基于马尔可夫链理论还首次尝试了数值计算方法.这一方法避免了复杂方程的求解困难,所以较有普适性,因此可用于研究更为复杂的网络模型.我们用这种数值计算方法研究了一个具有对数增长的加速增长模型,这个模型也能自组织演化成SF网络.     

Novel hybrid mitigation strategy for improving the resiliency of hierarchical networks subjected to attacks

This paper studies the resiliency of hierarchical networks when subjected to random errors, static attacks, and cascade attacks. The performance is compared with existing Erdös–Rényi (ER) random networks and Barabasi and Albert (BA) scale-free networks using global efficiency as the common performance metric. The results show that critical infrastructures modeled as hierarchical networks are intrinsically efficient and are resilient to random errors, however they are more vulnerable to targeted attacks than scale-free networks. Based on the response dynamics to different attack models, we propose a novel hybrid mitigation strategy that combines discrete levels of critical node reinforcement with additional edge augmentation. The proposed modified topology takes advantage of the high initial efficiency of the hierarchical network while also making it resilient to attacks. Experimental results show that when the level of damage inflicted on a critical node is low, the node reinforcement strategy is more effective, and as the level of damage increases, the additional edge augmentation is highly effective in maintaining the overall network resiliency.     

度关联无标度网络上的有倾向随机行走

有倾向随机行走是研究网络上数据包路由策略的有效方法. 由于许多真实技术网络包括互联网都具有负的度关联特征, 因此本文研究这种网络上的有倾向随机行走性质. 研究表明: 在负关联网络上粒子可以在连接度较大的节点上均匀分布, 而连接度小的节点上粒子较少; 负关联网络上随机行走的速度比非关联网络更快; 找到了负关联网络上的最佳倾向性系数, 在此情况下负关联网络上随机行走的速度远快于非关联网络. 负关联网络既可以利用度小的节点容纳粒子, 又可以利用度大的节点快速传输, 这是负关联网络上高行走效率产生的机制.