基于移动模型的车载自组织网络连通特征

针对车载自组织网络(VANET)的连通性问题,分析了其拓扑动态变化过程中网络的连通性的演化特征。首先,提出以连通分支数、连通概率及连通长度为评价指标的VANET拓扑连通性参数;然后,结合车辆换道功能的智能驾驶移动模型(IDM-LC),应用VanetMobiSim仿真软件建立VANET;最后,通过仿真实验分析了节点通信半径与平均连通分支数、平均连通率及平均连通长度之间的关系,同时分析了VANET连通分支数的统计分布特征,用Q-Q图和T检验验证得出结论:连通分支数服从正态分布,且该统计分布特征与节点通信半径无关。     

基于随机图的物联网多节点连通性判定方法

多节点协作已成为物联网的重要应用趋势,为了确保多个节点之间的可靠协作,提出了一种基于随机图的多节点连通性判定方法.首先,根据节点之间的直接连通概率构建节点连通性随机图;然后将所构建的随机图拆分为包含协作组节点的目标组件和不包含协作组节点的中继组件,从而将协作组内多节点之间的连通性转换为组件之间的连通性;最后,提出了测试组件之间连通性的算法,其中定义了与直接连通概率有关的测试规则以对组件之间测试边的测试优先级进行排序.仿真结果表明,相比于现有方法,所提方法能够以较低的成本获得较高的准确度.     

移动机器人全覆盖路径规划优化方法

从全局优化的角度出发,提出一种基于子区域分割的环境建模方法,将各个子区域缩成节点、子区域重心间距离缩成节点间连线,建立起含障区域的全连通图——广义距离矩阵模型,对Hopfield神经网络求解旅行商问题的动态规划方法进行改进后,将其用于求该模型的最优有向连通图。机器人沿最短路径完成覆盖,就是按最优顺序走过连通图中所有节点并依次覆盖各节点所代表的子区域,从而完成移动机器人在含障区域内的全覆盖路径规划问题的优化解决方案。     

WSN的对偶密钥路径本地搜索算法

无线传感器网络中基于规则网络的密钥预置方案具有预置开销小、寻径快等特点,但这类方案直接对偶密钥建立的概率较低。该文基于已经存在的节点连通集团,针对传感器网络对偶密钥连通图,提出层次连通框架,定义节点层次连通度,给出节点本地搜索算法。仿真结果表明该算法能提高节点的平均密钥连通度。     

最小连通支配集问题的化简算法

分析连通支配集的支配性约束和连通性约束条件,提出2条针对简单无向连通图最小连通支配集问题的化简规则.规则通过对图中节点的邻节点进行分类以及寻找图的割点提前确定一些必选节点,同时删除一些多余节点,从而降低原问题的规模.从理论上证明了化简规则的正确性,并通过随机仿真实验验证化简规则的有效性.     

基于拓扑法的多关节机械手无碰路径规划

本文提出了一种基于拓扑法的多关节机械手无碰路径规划方法,文中重点讨论了三关节机械手路径规划时如何划分状态空间、判别各连通块之间的连通性以及路径搜索等问题,最后我们给出了一个实验结果以示该方法的有效性。     

基于Markov链模型的MANET连通性分析

提出一种基于Markov链模型的移动Ad hoc网络（MANET）连通性分析方法。建立节点可靠性分析的Markov链模型,使之便于计算节点的可靠性概率。基于此,建立网络剩余节点数以及故障节点数状态转移的Markov链模型,并推导出计算节点随机网络连通概率的公式。通过Matlab仿真验证了理论分析的正确性。     

无线传感器网络的连通模型

针对无线传感器网络连通性研究中缺乏数学模型的问题,在对网络节点的连通关系进行数学描述的基础上,提出一种无线传感器网络的连通模型,模型将单个节点的状态信息表示为一个向量,包含节点的工作状态、位置状态、能量状态及节点之间连通关系信息,整个网络表示为由各节点状态向量组成的矩阵.通过举例说明了模型在分析网络连通拓扑结构和网络通讯能量控制问题上的适用性.该模型的提出为从数学角度描述和评估无线传感器网络的连通性能提供了一个平台.关     

随机网络的连通率研究

随机图是一种简单并且可用于抽象现实社会多种实际系统的网络。与其他网络模型不同,随机图的构造方式决定其节点具有对等性,且网络中可能存在孤立节点和子图。对随机图尤其是其连通性的研究有助于更深入地了解具有随机连接特性及节点对等特性的真实网络。文章采用理论与仿真相结合的方法,重点研究随机图的连通性和随机图连通率的计算方法,揭示了随机图在演化过程中的形态变化,表明随机图中树结构的广泛存在。研究还发现,在巨大连通子图形成前,随机图的子图大小呈幂律分布。本研究结果为复杂网络相关的实证研究和性质复杂的网络相变态研究提供了理论依据。     

利用游程集合的标号传播实现快速连通域标记

为了实现对图像的快速连通域标记,提出一种基于传播游程集合标号的二值图像连通域标记算法.该算法仅对每个由一系列相邻行中的连通游程所构成的游程集合(称为向下连通分支)而非游程分配临时标号,利用一个位置映射表一次性建立向下连通分支中所有游程与其共同临时标号之间的位置关联,将所有向下连通分支的标号构成一个规模很小的具有树形结构的等价信息表;再使等价信息直接在部分路径中传播,并通过最后一次标号表扫描将所有临时标号转换为代表标号.实验结果表明,文中算法原理和实现简单,且由于具有处理的等价信息量小、对向下连通分支内的游程标记操作少,以及在连通分支合并时无需计算最小标号等特点,使其速度快于现有算法.     

Graffinity: Visualizing Connectivity in Large Graphs

Multivariate graphs are prolific across many fields, including transportation and neuroscience. A key task in graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node‐link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Query‐based techniques for analyzing connectivity in graphs, however, can also easily suffer from cluttering if the query result is big enough. To remedy this, we introduce techniques that provide an overview of the connectivity and reveal details on demand. We have two main contributions: (1) two novel visualization techniques that work in concert for summarizing graph connectivity; and (2) Graffinity, an open‐source implementation of these visualizations supplemented by detail views to enable a complete analysis workflow. Graffinity was designed in a close collaboration with neuroscientists and is optimized for connectomics data analysis, yet the technique is applicable across domains. We validate the connectivity overview and our open‐source tool with illustrative examples using flight and connectomics data.     

Supercube: An optimally fault tolerant network architecture

Summary A new class of interconnection network topology is proposed for parallel and distributed processing. The attractive features of this class include (a) the network can be constructed for any number of computing nodes, (b) the network is incrementally expandable, i.e., a new node can easily be added to the existing network, (c) it has good fault-tolerant characteristics (measured by the connectivity of the network graph) and (d) it has small delay characteristics (measured by the diameter of the network graph). The node connectivity of the network is equal to the minimum node degree. In this sense the network is optimally fault-tolerant.     

Node connectivity and arc connectivity of a fuzzy graph

The fuzzy graph approach is more powerful in cluster analysis than the usual graph - theoretic approach due to its ability to handle the strengths of arcs effectively. The concept of node-strength sequence is introduced and is studied in a complete fuzzy graph. Two new connectivity parameters in fuzzy graphs namely, fuzzy node connectivity (κ) and fuzzy arc connectivity (κ^′) are introduced and obtained the fuzzy analogue of Whitney’s theorem. Fuzzy node cut, fuzzy arc cut and fuzzy bond are defined. Fuzzy bond is a special type of a fuzzy bridge. It is proved that at least one of the end nodes of a fuzzy bond is a fuzzy cutnode. It is shown that κ=κ^′ for a fuzzy tree and it is the minimum of the strengths of its strong arcs. The relationships of the new parameters with already existing vertex and edge connectivity parameters are studied and is shown that the value of all these parameters are equal in a compete fuzzy graph. Also a new clustering technique based on fuzzy arc connectivity is introduced.     

无线传感器网络的连通覆盖临界条件分析

连通与覆盖控制作为无线传感器网络中两个最基本的问题,取决于网络配置及节点的传播距离,反映了网络的感知质量与资源的优化分配。为了用最优化数量的传感器节点来改善和确保网络的连通与覆盖,在分析现有研究成果的基础上,提出了满足渐进连通覆盖的临界充分条件及必要条件。理论分析及仿真实验均表明,提出的临界条件更紧凑和规则化,有助于对无线传感器网络进行更细致的研究。     

A note on connectivity of multi-agent systems with proximity graphs and linear feedback protocol

In this note, we investigate the possibility of simplifying the connectivity verification for multi-agent systems with proximity graphs and the linear feedback protocol. A Lyapunov-like approach is developed for analyzing the monotonicity of the largest edge length in the network. Based on this approach, we provide an initial-connectivity-based sufficient condition for dynamic connectivity. In particular, when each initial node degree is not less than three fourth of the graph order, initial network connectivity implies dynamic connectivity.     

有序势博弈及其在智能体无线网络中的应用

有序势博弈具有广泛的应用,势有向图中不含单向圈是判定有序势博弈的一个充分必要条件.利用矩阵半张量积和置换矩阵,通过博弈的支付矩阵求取势有向图的邻接矩阵.通过收缩势有向图中的双向圈,将单向圈的存在性问题转化为判断收缩后的图中圈的存在性问题.此外,分析有序势函数的一些基本性质,并给出有序势函数的具体计算方法.最后结合线性规划讨论了有序势博弈在延长智能体无线网络系统寿命的应用.     

Network fault diagnosis using a realistic abductive reasoning model

This paper proposes a method to solve the network fault diagnosis problem using the Realistic Abductive Reasoning Model. This model uses an abductive inference mechanism based on the parsimonious covering theory, and adds some new features to the general model of diagnostic problem-solving. The network fault-diagnosis knowledge is assumed to be represented in the form of causal chaining, namely, a hyper-bipartite graph. A layered graph is constructed from the given hyper-bipartite graph by the addition of a few dummy nodes. Then the diagnostic problem is solved, starting from the lowest layer of the layered graph, as a series of bipartite graphs, until the top-most layer is reached. The inference mechanism uses a Realistic Abductive Reasoning Model to diagnose the faults in a communication network, which is symptom-driven, based on some application programs. The hypothesis-test paradigm is used to refine the solution space. The fault-diagnostic capability of the proposed inference model is demonstrated by considering one node of a given network where the management information would be used to diagnose its local problems and the connectivity of the node in the network. The results obtained by the proposed model substantiate its effectiveness in solving network fault-diagnostic problems.     

融合知识图谱的双线性图注意力网络推荐算法

知识图谱可有效缓解传统协同过滤中的数据稀疏和冷启动问题,因此,近年来在推荐系统中融入知识图谱的方法成为重要的探索方向。然而现有的方法大多将知识图谱的网络结构划分为单独路径或仅利用了一阶邻居信息,造成无法建立整个图上的高阶连通性问题。为解决该问题,提出融合知识图谱和图注意力网络的KG-BGAT模型,并设计了双线性采集器。双线性采集器能够在信息采集阶段获取节点间的特征交互信息,丰富节点表示;图注意力网络通过递归嵌入传播算法将各个节点表示沿图进行传播,能够捕获图中的高阶连通性。在MovieLens-1M数据集上进行了Top-K推荐实验,在推荐列表长度为20时,精确率、召回率和归一化折损累计增益分别为29.4%、24.9%、67.4%,超过了目前主流的CKE、RippleNet、KGCN等融合知识图谱的推荐算法。实验证明提出的方法能够有效提高推荐结果的准确性。