基于自适应-脉冲控制方法实现时变耦合驱动-响应复杂网络的投影同步

最近,对时变延迟网络的脉冲稳定性的研究大量出现,但通过自适应-脉冲控制方法获得的时变延迟网络同步准则却很少.本文中,运用自适应-脉冲控制方法,设计自适应反馈控制器、自适应律和线性脉冲控制器,研究时变耦合部分线性系统驱动-响应复杂网络的投影同步.获得时变耦合网络的自适应-脉冲投影同步准则.并且不需要网络的耦合构造矩阵是不可约的.另外,运用数值模拟证实方案的有效性和可行性.     

基于超图结构的科研合作网络演化模型

基于科研论文作者的合作方式, 用超图理论构建了一个科研合作超网络演化模型. 利用平均场理论分析了作者发表论文的演化规律, 发现作者的超度 (即发表论文数) 分布符合幂律分布. 进一步理论分析得到分布的幂指数γ与合作领域作者增长速度相关. γ越大, 新作者增长速度越快, 且存在关系: γ=1+L/M (L/M为作者增长率). 并通过对《物理学报》与《中国科学》2003–2012年期间作者发表论文进行了数据分析, 实证结果与理论分析及模拟结果能很好地符合. 本文对科研合作网络的理论和实证研究有一定的借鉴意义. 关键词： 复杂网络 超图 科研合作网络 演化模型     

一种基于布朗粒子的混合搜索模型

基于网络上的布朗粒子运动基本原理,提出了一种单粒子和多粒子相结合的混合搜索模型.该模型将一次搜索过程分成单粒子搜索与多粒子搜索两个阶段,既克服了单粒子搜索效率低下的缺点,又降低了多粒子搜索的硬件代价.在各种复杂网络拓扑上实施该模型,并与混合导航模型进行比较.结果表明,混合搜索模型的平均搜索时间收敛更快,硬件代价更小.将度大优先的目标选择策略与混合搜索模型相结合,能进一步提高搜索效率.此外通过仿真发现,在无标度网络上混合搜索模型的效率远高于单粒子随机行走,与多粒子随机行走的效率相当,但硬件代价远小于多粒子行走.最后针对该模型给出了一种能有效降低负载的"吸收"策略.     

噪声环境下时滞耦合网络的广义投影滞后同步

时滞和噪声在复杂网络中普遍存在,而含有耦合时滞和噪声摄动的耦合网络同步的研究工作却极其稀少. 本文针对噪声环境下具有不同节点动力学、不同拓扑结构及不同节点数目的耦合时滞网络,提出了两个网络之间的广义投影滞后同步. 首先,构建了更加贴近现实的驱动-响应网络同步的理论框架;其次,基于随机时滞微分方程LaSalle不变性原理,严格证明了在合理的控制器作用下,驱动网络和响应网络在几乎必然渐近稳定性意义下能够取得广义投影滞后同步;最后,借助于计算机仿真,通过具体的网络模型验证了理论推理的有效性. 数值模拟结果表明,驱动网络与响应网络不但能够达到广义投影滞后同步,而且同步效果不依赖于耦合时滞和比例因子的选取,同时也揭示了更新增益和耦合时滞对同步收敛速度的显著性影响. 关键词： 复杂网络 广义投影滞后同步 随机噪声 时滞     

基于社团结构的城市地铁网络模型研究

本文运用复杂网络理论, 对我国北京、上海、广州和深圳等城市的地铁网络进行了实证研究. 分别研究了地铁网络的度分布、聚类系数和平均路径长度. 研究表明, 该网络具有高的聚类系数和短的平均路径长度, 显示小世界网络的特征, 其度分布并不严格服从幂律分布或指数分布, 而是呈多段的分布, 显示层次网络的特征. 此外, 它还具有重叠的社团结构特征. 基于实证研究的结果, 提出一种基于社团结构的交通网络模型, 并对该模型进行了模拟分析, 模拟结果表明, 该模型的模拟结果与实证研究结果相符. 此外, 该模型还能解释其他类型的复杂网络(如城市公共汽车交通网络)的网络特性. 关键词： 复杂网络 地铁网络 小世界 社团     

记忆粒子模型中包含复杂粒子炮弹与出射粒子的预平衡发射研究

本文讨论了记忆粒子模型中复杂粒子发射几率及复杂炮弹入射引起的初值问题;提出了m-r系合作发射机制;计算结果与实验符合良好.     

双复杂网络间的演化博弈

复杂网络的演化博弈是社会结构与稳定的重要模型. 基于单网络演化博弈模型, 提出了一种双复杂动态网络的演化博弈模型, 考虑双复杂网络在两个不同收益矩阵的囚徒困境博弈下增长, 当两个网络没有相互联系时, 发现增长网络中的空间互利性所导致的平均合作水平的突变, 推广了前人的结论. 在两个网络有相互联系时, 平均合作水平可以两者出现高度同步. 在网络的收益系数达到一定时, 才实现较高的合作水平. 增加网络内连接数量时, 自然选择不利于网络的合作, 而公平选择却有利于网络的合作, 说明了更新策略的影响. 当增加网络间连接数量时, 两个网络合作水平都下降. 当保持网络间和网络内的连接比例不变时, 网络的平均度越大, 平均合作水平越小. 本文发现了背叛领袖的存在, 并揭示了双网络模型下背叛领袖对平均合作水平的影响及其与合作领袖的互动机理, 这结果给出社会结构, 稳定和演化的重要信息和启示.     

关联复杂网络建模及辨识研究

运用异质耦合拆分方法和驱动-响应模型,提出关联复杂网络节点参数和拓扑结构的辨识方法.首先,研究异质关联复杂网络建模方法,进而依据网络耦合性质不同,拆分构造了两类异质关联复杂网络.然后运用驱动-响应模型、LaSalle不变原理和Gram矩阵,设计节点系统参数和拓扑参数的自适应辨识观测器.所提的观测器能在线获取网络的节点参数、不同耦合性质的拓扑参数.最后,通过数值仿真验证所提方法的有效性.     

静态随机存储器单粒子翻转效应三维数值模拟

针对特征尺寸为1.5 μm的国产静态随机存储器（SRAM）,构建了三维SRAM存储单元模型,并对重离子引起的SRAM单粒子翻转效应进行了数值模拟.计算并分析了单粒子引起的单粒子翻转和电荷收集的物理图像,得到了SRAM器件的单粒子翻转截面曲线.单粒子翻转的数值模拟结果与重离子微束、重离子宽束实验结果比较一致,表明所建立的三维器件模型可以用来研究SRAM器件的单粒子翻转效应. 关键词： 三维数值模拟 单粒子翻转 微束 宽束     

无标度立体Koch网络上随机游走的平均吸收时间

作为一种基本的动力学过程,复杂网络上的随机游走是当前学术界研究的热点问题,其中精确计算带有陷阱的随机游走过程的平均吸收时间(mean trapping time,MTT)是该领域的一个难点.这里的MTT定义为从网络上任意一个节点出发首次到达设定陷阱的平均时间.本文研究了无标度立体Koch网络上带有一个陷阱的随机游走问题,解析计算了陷阱置于网络中度最大的节点这一情形的网络MTT指标.通过重正化群方法,利用网络递归生成的模式,给出了立体Koch网络上MTT的精确解,所得计算结果与数值解一致,并且从所得结果可以看出,立体Koch网络的MTT随着网络节点数N呈线性增长.最后,将所得结果与之前研究的完全图、规则网络、Sierpinski网络和T分形网络进行比较,结果表明Koch网络具有较高的传输效率.     

基于多路可视图的健康与心梗患者心电图信号复杂网络识别

可视图(visibility graph, VG)算法已被证明是将时间序列转换为复杂网络的简单且高效的方法,其构成的复杂网络在拓扑结构中继承了原始时间序列的动力学特性.目前,单维时间序列的可视图分析已趋于成熟,但应用于复杂系统时,单变量往往无法描述系统的全局特征.本文提出一种新的多元时间序列分析方法,将心梗和健康人的12导联心电图(electrocardiograph, ECG)信号转换为多路可视图,以每个导联为一个节点,两个导联构成可视图的层间互信息为连边权重,将其映射到复杂网络.由于不同人群的全连通网络表现为完全相同的拓扑结构,无法唯一表征不同个体的动力学特征,根据层间互信息大小重构网络,提取权重度和加权聚类系数,实现对不同人群12导联ECG信号的识别.为判断序列长度对识别效果的影响,引入多尺度权重度分布熵.由于健康受试者拥有更高的平均权重度和平均加权聚类系数,其映射网络表现为更加规则的结构、更高的复杂性和连接性,可以与心梗患者进行区分,两个参数的识别准确率均达到93.3%.     

A Quantum Walk Model for Idea Propagation in Social Network and Group Decision Making

We propose a quantum walk model to investigate the propagation of ideas in a network and the formation of agreement in group decision making. In more detail, we consider two different graphs describing the connections of agents in the network: the line graph and the ring graph. Our main interest is to deduce the dynamics for such propagation, and to investigate the influence of compliance of the agents and graph structure on the decision time and the final decision. The methodology is based on the use of control-U gates in quantum computing. The original state of the network is used as controller and its mirrored state is used as target. The state of the quantum walk is the tensor product of the original state and the mirror state. In this way, the proposed quantum walk model is able to describe asymmetric influence between agents.     

Network evolution driven by dynamics applied to graph coloring

An evolutionary network driven by dynamics is studied and applied to the graph coloring problem. From an initial structure, both the topology and the coupling weights evolve according to the dynamics. On the other hand, the dynamics of the network are determined by the topology and the coupling weights, so an interesting structure-dynamics co-evolutionary scheme appears. By providing two evolutionary strategies, a network described by the complement of a graph will evolve into several clusters of nodes according to their dynamics. The nodes in each cluster can be assigned the same color and nodes in different clusters assigned different colors. In this way, a co-evolution phenomenon is applied to the graph coloring problem. The proposed scheme is tested on several benchmark graphs for graph coloring.     

Efficient Network Dismantling via Node Explosive Percolation

The network dismantling problem asks the minimum separate node set of a graph whose removal will break the graph into connected components with the size not larger than the one percentage of the original graph.This problem has attracted much attention recently and a lot of algorithms have been proposed. However, most of the network dismantling algorithms mainly focus on which nodes are included in the minimum separate set but overlook how to order them for removal, which will lead to low general efficiency during the dismantling process. In this paper,we reformulate the network dismantling problem by taking the order of nodes' removal into consideration. An efficient dismantling sequence will break the network quickly during the dismantling processes. We take the belief-propagation guided decimation(BPD) dismantling algorithm, a state-of-the-art algorithm, as an example, and employ the node explosive percolation(NEP) algorithm to reorder the early part of the dismantling sequence given by the BPD. The proposed method is denoted as the NEP-BPD algorithm(NBA) here. The numerical results on Erd¨os-R′enyi graphs,random-regular graphs, scale-free graphs, and some real networks show the high general efficiency of NBA during the entire dismantling process. In addition, numerical computations on random graph ensembles with the size from 2~(10) to2~(19) exhibit that the NBA is in the same complexity class with the BPD algorithm. It is clear that the NEP method we used to improve the general efficiency could also be applied to other dismantling algorithms, such as Min-Sum algorithm,equal graph partitioning algorithm and so on.     

Ricci Curvature-Based Semi-Supervised Learning on an Attributed Network

In recent years, on the basis of drawing lessons from traditional neural network models, people have been paying more and more attention to the design of neural network architectures for processing graph structure data, which are called graph neural networks (GNN). GCN, namely, graph convolution networks, are neural network models in GNN. GCN extends the convolution operation from traditional data (such as images) to graph data, and it is essentially a feature extractor, which aggregates the features of neighborhood nodes into those of target nodes. In the process of aggregating features, GCN uses the Laplacian matrix to assign different importance to the nodes in the neighborhood of the target nodes. Since graph-structured data are inherently non-Euclidean, we seek to use a non-Euclidean mathematical tool, namely, Riemannian geometry, to analyze graphs (networks). In this paper, we present a novel model for semi-supervised learning called the Ricci curvature-based graph convolutional neural network, i.e., RCGCN. The aggregation pattern of RCGCN is inspired by that of GCN. We regard the network as a discrete manifold, and then use Ricci curvature to assign different importance to the nodes within the neighborhood of the target nodes. Ricci curvature is related to the optimal transport distance, which can well reflect the geometric structure of the underlying space of the network. The node importance given by Ricci curvature can better reflect the relationships between the target node and the nodes in the neighborhood. The proposed model scales linearly with the number of edges in the network. Experiments demonstrated that RCGCN achieves a significant performance gain over baseline methods on benchmark datasets.     

Topology of the Erasmus student mobility network

The collaboration network generated by the Erasmus student mobilities in the year 2003 is analyzed and modeled. Nodes of this bipartite network are European universities and links are the Erasmus mobilities between these universities. This network is a complex directed and weighted graph. The non-directed and non-weighted projection of this network does not exhibit a scale-free nature, but proves to be a small-word type random network with a giant component. The connectivity data indicates an exponential degree distribution, a relatively high clustering coefficient and a small radius. It can be easily modeled by using a simple configuration model and arguing the exponential degree distribution. The weighted and directed version of the network can also be described by means of simple random network models.     

Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.     

The physical position neighbourhood evolving network model

Many social, technological, biological and economical systems are properly described by evolved network models. In this paper, a new evolving network model with the concept of physical position neighbourhood connectivity is proposed and studied. This concept exists in many real complex networks such as communication networks. The simulation results for network parameters such as the first nonzero eigenvalue and maximal eigenvalue of the graph Laplacian, clustering coefficients, average distances and degree distributions for different evolving parameters of this model are presented. The dynamical behaviour of each node on the consensus problem is also studied. It is found that the degree distribution of this new model represents a transition between power-law and exponential scaling, while the Barábasi-Albert scale-free model is only one of its special (limiting) cases. It is also found that the time to reach a consensus becomes shorter sharply with increasing of neighbourhood scale of the nodes.     

拟态物理学优化的认知无线电网络频谱分配

针对认知无线电网络中基于图着色模型的频谱分配问题,基于其非确定性多项式特性,以最大化网络收益总和为目标,提出了一种基于拟态物理学优化的求解算法. 在拟态物理学优化算法中,将频谱分配问题的解映射为一个具有质量的微粒,通过建立微粒的质量与其适应值之间的关系,并利用万有引力定律定义微粒间的虚拟作用力的大小,使整个群体向更好的方向运动,实现群体寻优. 给出了频谱分配问题的具体求解过程,并根据分配问题的二进制编码特点,改进了微粒的位置更新方程. 仿真实验表明：本文算法能更好地实现网络收益最大化. 关键词： 拟态物理学优化 认知无线电网络 频谱分配 网络收益     

Network synchronizability analysis: The theory of subgraphs and complementary graphs

In this paper, subgraphs and complementary graphs are used to analyze network synchronizability. Some sharp and attainable bounds are derived for the eigenratio of the network structural matrix, which characterizes the network synchronizability, especially when the network’s corresponding graph has cycles, chains, bipartite graphs or product graphs as its subgraphs.