Ranking important nodes in complex networks by simulated annealing

In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented.First, the concept of an importance sequence（IS） to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks.

Evolutionary dynamics analysis of complex network with fusion nodes and overlap edges

Multiple complex networks, each with different properties and mutually fused, have the problems that the evolving process is time varying and non-equilibrium, network structures are layered and interlacing, and evolving characteristics are difficult to be measured. On that account, a dynamic evolving model of complex network with fusion nodes and overlap edges（CNFNOEs） is proposed. Firstly, we define some related concepts of CNFNOEs, and analyze the conversion process of fusion relationship and hierarchy relationship. According to the property difference of various nodes and edges, fusion nodes and overlap edges are subsequently split, and then the CNFNOEs is transformed to interlacing layered complex networks（ILCN）. Secondly,the node degree saturation and attraction factors are defined. On that basis, the evolution algorithm and the local world evolution model for ILCN are put forward. Moreover, four typical situations of nodes evolution are discussed, and the degree distribution law during evolution is analyzed by means of the mean field method.Numerical simulation results show that nodes unreached degree saturation follow the exponential distribution with an error of no more than 6%; nodes reached degree saturation follow the distribution of their connection capacities with an error of no more than 3%; network weaving coefficients have a positive correlation with the highest probability of new node and initial number of connected edges. The results have verified the feasibility and effectiveness of the model, which provides a new idea and method for exploring CNFNOE＇s evolving process and law. Also, the model has good application prospects in structure and dynamics research of transportation network, communication network, social contact network,etc.

Evidential method to identify influential nodes in complex networks

Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality mea- sure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degree centrality, betweenness centra- lity and closeness centrality are taken into consideration in the proposed method. Numerical examples are used to illustrate the effectiveness of the proposed method.

Detecting overlapping communities based on vital nodes in complex networks

Detection of community structures in the complex networks is significant to understand the network structures and analyze the network properties. However, it is still a problem on how to select initial seeds as well as to determine the number of communities. In this paper, we proposed the detecting overlapping communities based on vital nodes algorithm（DOCBVA）, an algorithm based on vital nodes and initial seeds to detect overlapping communities. First, through some screening method, we find the vital nodes and then the seed communities through the pretreatment of vital nodes. This process differs from most existing methods, and the speed is faster. Then the seeds will be extended. We also adopt a new parameter of attribution degree to extend the seeds and find the overlapping communities. Finally, the remaining nodes that have not been processed in the first two steps will be reprocessed. The number of communities is likely to change until the end of algorithm. The experimental results using some real-world network data and artificial network data are satisfactory and can prove the superiority of the DOCBVA algorithm.

Rapid identifying high-influence nodes in complex networks

A tiny fraction of influential individuals play a critical role in the dynamics on complex systems. Identifying the influential nodes in complex networks has theoretical and practical significance. Considering the uncertainties of network scale and topology, and the timeliness of dynamic behaviors in real networks, we propose a rapid identifying method（RIM）to find the fraction of high-influential nodes. Instead of ranking all nodes, our method only aims at ranking a small number of nodes in network. We set the high-influential nodes as initial spreaders, and evaluate the performance of RIM by the susceptible-infected-recovered（SIR） model. The simulations show that in different networks, RIM performs well on rapid identifying high-influential nodes, which is verified by typical ranking methods, such as degree, closeness, betweenness,and eigenvector centrality methods.

Identifying influential nodes based on graph signal processing in complex networks

Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However, node heterogeneity （i.e., different attributes such as interest, energy, age, and so on ） ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal pro- cessing based centrality （GSPC） method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.

Coarse Graining Method Based on Noded Similarity in Complex Network

Coarse graining of complex networks is an important method to study large-scale complex networks, and is also in the focus of network science today. This paper tries to develop a new coarse-graining method for complex networks, which is based on the node similarity index. From the information structure of the network node similarity, the coarse-grained network is extracted by defining the local similarity and the global similarity index of nodes. A large number of simulation experiments show that the proposed method can effectively reduce the size of the network, while maintaining some statistical properties of the original network to some extent. Moreover, the proposed method has low computational complexity and allows people to freely choose the size of the reduced networks.

RESEARCH ON KEY NODES OF WIRELESS SENSOR NETWORK BASED ON COMPLEX NETWORK THEORY

On the basis of complex network theory, the issues of key nodes in Wireless Sensor Networks (WSN) are discussed. A model expression of sub-network fault in WSN is given at first; subsequently, the concepts of average path length and clustering coefficient are introduced. Based on the two concepts, a novel attribute description of key nodes related to sub-networks is proposed. Moreover, in terms of node deployment density and transmission range, the concept of single-point key nodes and generalized key nodes of WSN are defined, and their decision theorems are investigated.

航空器场面滑行关键路径识别与抗毁性研究

为准确辨识机场地面滑行系统重要路段,提升拥塞风险控制能力,通过构建机场活动区交通网络模型,从节点近邻、全局路径、特征向量和网络位置4个角度挖掘关键节点,提出改进介数中心性算法,得到节点重要度排序样例集。综合鲁棒性、性能下降速率以及单位时间性能损失等网络性能指标,通过抗毁性实验分析性能变化曲线,确定综合抗毁性衡量指标ki测度下复杂网络节点重要度排序结果,实现关键路径识别。以华北某机场为例开展实证研究,结果表明:机场活动区交通网络具有较小的聚类系数,在随机攻击下抗毁性较强,基于度中心性的节点序列为所有样例中最满足实际运行中的关键路段,管制员可实施重点路段流量调配,抑制冲突。

西安城市展示中心长悬挑、大跨度钢结构设计

西安城市展示中心由南馆、北馆和连桥组成,其中南馆悬挑长度62m,连桥跨度150m。结合建筑方案特点,南馆与连桥采用整层钢桁架结构体系,结构布置与建筑形态高度契合。介绍了南馆、连桥、穹顶、屋面等位置的结构布置情况,明确了超限项和抗震性能目标,对于多杆件复杂交汇钢节点进行有限元分析。针对南馆、连桥屋楼盖振动舒适度不足,设计布置了调谐质量阻尼器(TMD),实测减振效果显著。对连桥提升过程进行施工模拟,结果表明该过程增加了连桥挠度,需要考虑提前起拱。对超长连体结构温度和地震行波效应进行分析,根据结果对连桥楼板配筋和局部框架柱进行了加强。大震弹塑性分析与防连续倒塌分析结果表明,结构满足“大震不倒”的性能目标,结构冗余度较高,具有良好的防连续倒塌能力。

基于邻域K-shell分布的关键节点识别方法

复杂网络中关键节点的精准识别对于网络结构稳定和信息传播起着至关重要的作用。传统K-shell方法仅通过节点在网络中所处位置对节点的重要性进行评估,导致区分度不高。基于此,综合考虑了节点的全局信息和局部信息对节点重要性的影响,提出一种基于邻域K-shell分布的关键节点识别方法。该方法通过节点邻域Ks值定义节点的熵,从而反映邻居节点的K-shell分布特征。通过11个网络数据集上的仿真实验,验证了所提方法能够更准确地识别并区分复杂网络中的关键节点。

Impulsive synchronization of two coupled complex networks with time-delayed dynamical nodes

In this paper, we investigate the impulsive synchronization between two coupled complex networks with time- delayed dynamical nodes. Based on the Lyapunov stability, the linear feedback control and the impulsive control theories, the linear feedback and the impulsive controllers are designed separately. By using the generalized Barbalat＇s lemma, the global asymptotic impulsive synchronization of the drive-response complex networks is derived and some corresponding sufficient conditions are also obtained. Numerical examples are presented to verify the effectiveness and the correctness of the synchronization criteria.

基于深度强化学习的复杂网络节点影响力排序算法

针对当前节点影响力评估算法准确度较低的情况,提出了一种基于深度强化学习的节点影响力排序算法。该算法从网络拆解的视角看待节点影响力,将节点影响力的排序问题转换为网络拆除策略的优化问题。算法首先利用排序学习训练图神经网络模型的节点特征提取能力,然后使用强化学习对依赖于网络状态的节点断连行为做价值学习,最后使用训练完成的模型预测网络拆除的最佳策略,即节点影响力的最准确排序。仿真实验证明,所提算法在典型真实数据集的CN(Crtical Node)与ND(Network Dismantling)问题上,相较于PageRank算法,准确度分别提升了31.1%与29.0%。同时,该算法具有较低的复杂度,可为网络稳定性分析和网络性能优化提供技术支撑。

基于光学定位的三维超声引导肌肉注射仪设计研究

目的解决光学定位技术在引入肌肉痉挛治疗的注射手术应用中,光学定位设备与现有医用超声仪器之间的不适配问题,旨在进一步提升理论定位精度稳定性,简化必需操作并降低实际应用难度。方法在现有超声仪及相关设备功能基础上,结合光学定位设备工作条件需求进行需求分析,重新构建产品元件功能流模型并映射为网络节点,并基于复杂网络节点重要度评价方法,确定目标产品设计重心点并分层设计,最后采用仿真验证测试设计合理性。结果实验结果符合预期假设,目标节点所对应功能元件与其它节点的相关性程度与目标元件所需兼容连接的复杂度呈正相关。基于实验得出能够适配光学定位仪与超声设备的设计方案,有效解决二维超声导航引导肌肉注射所面临的不直观、不可视等问题。结论基于复杂网络节点的重要度评价方法更适用于复杂功能产品的设计研究,评价结果重要度差异更为显著。

水网结构对多灾害情景的韧性响应研究

通过识别基础设施系统内部重要节点以提高基础设施韧性,对增强城市韧性具有重要意义。水网作为基础设施的重要组成部分,对水资源统筹调配与水旱灾害防控等发挥着重要作用。基于复杂网络理论,构建水网网络拓扑结构,计算水网各节点参数与灾害风险度,采用不同节点攻击策略,使用随机攻击、蓄意攻击和自然灾害3种攻击方式,得到整体网络效率与最大连通子图大小,对水网韧性进行分析。研究结果表明:水网结构遭到随机攻击或自然灾害时,破坏节点达80%左右,水网韧性趋近于0;水网结构遭到蓄意攻击时,破坏节点达50%左右,水网结构系统崩溃;在蓄意攻击中,PageRank攻击对水网韧性影响较大;在自然灾害攻击中,水网结构在面对暴雨洪涝时表现出较强的韧性。在网络拓扑结构研究水平的基础上,在工程真实物理状态下对水网结构遭到随机攻击、蓄意攻击与自然灾害后的水网韧性进行了更全面的分析与评价,为水网工程建设与管理研究提供参考。

中国省会航空节点城市复杂时空系统协调度变化及空间特征分析

航空运输业提升了城市系统的吸引力并激发新的交通需求,引导城市经济和空间结构快速演进。本文构建了包含经济、规模、外贸、科技、社会、环境、航空等要素在内的城市复杂时空系统指标体系,对中国省会航空节点城市及其构成的7个空管区的复杂时空系统发展的协调程度变化情况及时空聚类特征、空间溢出效应进行了分析。研究发现:2001—2019年,中国31个省会(首府、直辖市)航空节点城市在复杂时空系统发展过程中呈现了或分散、或集中的趋势特征,南方城市在研究末期的协调程度表现更好。7个空管区中,华北区协调度最高,新疆区、西北区、华东区、中南区的协调度水平发展增速较快,航空系统的平均协调度最高。各航空节点城市复杂时空系统协调度的时空聚类模式存在空间分异特征与聚类特征,航空系统在空管区的聚类优势最为突出,经济系统在空管区聚类辐射范围最大。航空、规模、科技与环境系统均表现了正向直接空间溢出效应,经济与科技系统表现了正向间接空间溢出效应。通过全面分析航空节点城市及空管区的复杂时空系统协调度变化,精准判定了航空节点城市多指标系统在区域发展过程中的影响程度及时空分布异质性,对拓展航空地理研究及促进航空节点城市高质量综合发展提供理论依据。

基于复杂网络的复杂产品系统关键节点辨识

复杂产品系统具有规模大、结构复杂等特点,从系统结构的视角辨识复杂产品系统关键节点,对于“事前”定位潜在风险源具有重要意义。针对目前“事后”追溯风险源难以保证复杂产品系统可靠运行及风险管理前瞻性等问题,考虑复杂产品系统的层级结构和子系统间风险的相互影响,提出了一种可用于“事前”风险管理的关键节点辨识方法。首先依据系统结构建立复杂网络;然后,构造零模型并结合网络统计特征初步划分节点集合,在此基础上引入线性阈值模型分析节点风险传播影响力,并基于风险的动态传播特征选取关键节点;最后,采用新舟700飞机的系统结构进行实证分析。结果表明,提出的方法有效地辨识了复杂产品系统的关键节点。

物联网环境下复杂光纤通信传输网络节点调度

为了解决物联网环境下,光纤通信传输网络节点能耗高、调度效率低的问题,提出一种物联网环境下复杂光纤通信传输网络节点调度方法。在物联网环境下,分析光纤通信传输网络节点的分布情况,根据动态联盟理论,以最快调度速度和最小投资为目标,构建复杂光纤通信传输网络节点调度模型。通过蚁群和粒子群算法相结合的融合算法展开全部搜索,获取最佳调度方案,实现光纤通信传输网络节点调度。实验结果表明,所提方法的节点平均能耗在6.0 mJ以下,LBF值始终高于0.20,且节点调度耗时始终低于200 ms,具有良好的调度效果。

气象灾害下的油气站场设备脆弱节点辨识方法

油气站场是保障我国石油与天然气等能源运输、储存体系的重要组成部分,油气站场的安全稳定运行是油气系统,乃至国家经济发展的重要一环。我国面积较大,包含多种气候地区,不同地域易发暴雨、雷电、台风等气象灾害。气象灾害发生在油气站场及其附近时可能会对相关设施造成破坏,导致站场处于异常工况中运行,甚至引发事故。常见的石油及相关液态加工产品的储运站场,介质除本身具有易燃易爆特性外,一般还具有一定的挥发性,易形成可燃气体云团。天然气站场常伴随高压运行条件,站场一旦出现异常工况或事故会使厂区内部工作人员的生命安全及周边环境都承受较大风险。气象灾害往往难以避免,但可以通过有针对性地增加安全措施加强站场在气象灾害条件下的稳定性,降低气象灾害带来的风险。为了达到上述目的,综合考虑油气站场的设备在遇到暴雨、雷暴等气象灾害时,更容易发生油气泄漏、爆炸等事故的情况,本文根据气象灾害发生场景,及相关场景下可能发生的灾害演化路径提出了一种定量的脆弱节点辨识方法。面对不同的环境风险,现有方法对于站场设备脆弱节点辨识缺乏精细度与准确度,错误辨识脆弱节节点不仅会导致防护过程中人力物力的浪费,更可能导致储罐管道的泄漏,甚至引发爆炸。本文对重力模型进行改进,结合鲁汶算法,提出基于社团分析模型的设备脆弱节点辨识方法。通过实验分析,得到暴雨天气、雷暴天气下的脆弱节点,并利用提出模型对历史案例进行分析,得到结果与实际调查情况较为吻合。相较于其他分析方法,社团分析模型精细度提高了10%以上,分析结果准确率提高了10%以上,召回率提高了14%以上。实验结果表明,本文所提方法可以实现多种极端天气下的脆弱节点辨识,且能对节点进行更精准的定位。