半边图模型之构造演算

提出一个动态可增殖的多层次自组织认知系统,每个层次具有形式上一致的知识表示方法,各层的自组关联、自组聚合、归约和样本表达四个知识处理模型是实现系统自组织层次增殖的核心模型。指出若要实现层次可自组织增殖的系统,其关键是要设计一个合理的聚合归约演算系统;提出一个适用于各个层次的基于可结合半边的自组图知识表示法,先给出自组图形式化的静态定义和动态定义,然后以自组关联模型为背景给出对应的自组图构造算法。     

半边图模型之多层次认知系统

针对具有多层次性和复杂性的认知问题提出一个动态可增殖的多层次自组织认知系统,每个层次具有形式上一致的知识表示方法,各层的自组关联、自组聚合、归约和样本表达四个知识处理模型是实现系统自组织层次增殖的核心模型。提出信息粒和容器的概念,信息粒演进流程模拟认知过程的静态归约,容器演进流程对应于认知的动态模拟,这两个流程在每个系统层次上对每个输入样本完成一个完整的模拟认知与归约表达。以自组图演算为理论模型,给出了每个层次内以及相邻层次之间的信息处理与传递的详细设计规范。     

泛函程序设计语言实现中的图归约演算

本文叙述了在Ｖｏｎ Ｎｅｕｍａｎｎ机器上实现基于λ演算，ＳＫＩ演算的泛函程序设计语言所采用的图归约演算。ＳＫＩ－Ｇ演算是ＳＫＩ演算的图形表示，是基于图形的形式归约系统，面向机器实现，是实现高阶，引用透明，归约语义，全惰性泛函程序设计语言的主要技术基础。     

基于细胞膜演算的Web服务事务处理形式化描述与验证

采用细胞膜演算具体分析了当前比较主流的Web服务中原子事务协调协议WS-AT．针对WS-AT协议采用简单的状态转换表和转换图,无法描述协调者和多个参与者的复杂协调活动,采用细胞膜演算给出了其形式化描述,用于规范协调者和参与者的活动,并分析了该协议的活性和安全性,得到了38187个状态．模型检验的实验结果表明,该协议满足稳定性、一致性和非平凡性,而不满足非阻塞性．进而,分析出注册和协调协议混在一起是其不满足非阻塞性的原因．     

Seal演算的偶图语义

偶图反应系统是一种新的理论工具,其基础是一种强调位置和连接的移动计算图形化模型--偶图,偶图范化了π演算和移动Ambient演算的特征,能够表示具有位置和移动性质的复杂系统.偶图反应系统为普适计算不同层次的设计和实现提供了统一的建模框架.Seal演算是一种用于描述移动计算的进程语言,具有良好的安全性质.文中给出了一种不带复制进程表达式的Seal演算的偶图表示,分析了该Seal演算与其偶图表示间的结构对应和操作对应.本研究扩展了偶图理论的应用范围,展示了偶图理论在描述安全演算方面的能力,为在偶图反应系统框架下研究Seal演算的性质和应用奠定了基础.     

抽象算法C—T—ABS和ABSTRACT的理论推导与等价性证明

ＡＢＳＴＲＡＣＴ，Ｃ－Ｔ－ＡＢＳ是把λ表达式转换成ＳＫＩ表达式的抽象算法，是函数式语言实现的理论基础。本文从λ演算，ＳＫＩ演算的基本理论出发，对这两个算法进行了理论推导及功能的等价证明。展示了对一输入，Ｃ－Ｔ－ＡＢＳ能生成较优化的代码，并对它们生成代码的长度及归约效率进行了比较。     

基于网络演算的无线自组网TCP丢包率上界研究

精确的TCP丢包率上界对无线环境下TCP协议的性能改进具有指导意义;对进入无线自组网节点的TCP数据流进行漏桶管制,节点为TCP流提供基于速率-延迟(Rate-Latency)模型的服务保障,在已有无线自组网链路吞吐量模型的基础上利用网络演算理论推导了TCP数据流的丢包率上界模型;在网格拓扑的无线自组网环境下,TCP数据流丢包率的仿真结果都在数值计算的上界范围之内,表明基于网络演算的无线自组网TCP丢包率性能上界模型具有较好的性能.     

无线自组网TCP吞吐量上界研究

对进入无线自组网节点的TCP数据流进行漏桶管制,节点为TCP提供基于速率-延迟模型的服务保障,在已有无线自组网链路吞吐量模型的基础上利用网络演算理论推导TCP数据流的吞吐量上界模型。在网格拓扑的无线自组网环境下,TCP数据流的仿真结果都在数值计算的上界范围内,从而表明基于网络演算的无线自组网TCP吞吐量性能上界模型具有较好的性能,同时也表明现有TCP协议在无线环境下还存在较大的改进空间。     

R-演算中若干问题的研究

李未教授提出了R-演算系统,它是形式理论的修正演算系统,是OPEN过程模式和GUINA过程模式的基础.R-演算在这2种过程模式中的核心作用是,当一个形式理论与事实产生矛盾时,找出矛盾的必要前提,从而获得一个协调的子理论.通过3种不同的方法细致刻画R-演算的基本概念"必要前提",第1种方法来自R-演算,第2种方法基于极大协调子集与极小非协调子集的,最后一种方法是对于R-必要前提的归纳定义.通过比较这3种方法,指出各自的优缺点,并从第3种方法推演出一个可靠并且相对完全的系统.在比较这3种方法的同时,还细致地探讨了R-终止式的上下界以及极大协调子集的不可枚举性.其中极大协调的不可枚举性在一定程度上表明了不存在一种同时满足可靠并且完全的系统.     

基于信息粒度和连通强度的优化学习

针对具有分布式网络和复杂的拓扑结构的认知学习问题,本文提出一种动态的基于信息粒度和连通强度的自组织的认知优化学习系统。每个网络节点的信息粒在高聚合度的情况下,具有信息表示的完整性,知识系统中节点的自组聚合和节点间的强连通性是优化学习绩效的核心模型。利用信息粒的聚合度和信息粒间的连通性的概念,信息粒度的演进流程模拟认知学习过程的静态归约,连接强度演进流程对应于认知学习的动态模拟,这两个流程在学习系统中对每个输入样本完成一个完整的模拟认知与归约表达。以分布式拓扑结构为理论模型,给出了每个节点信息粒度以及节点之间的信息处理与传递的认知优化规范。     

A sound and complete theory of graph transformations for service programming with sessions and pipelines

Graph transformation techniques, the Double-Pushout (DPO) approach in particular, have been successfully applied in the modeling of concurrent systems. In this area, a research thread has addressed the definition of concurrent semantics for process calculi. In this paper, we propose a theory of graph transformations for service programming with sophisticated features such as sessions and pipelines. Through graph representation of CaSPiS, a recently proposed process calculus, we show how graph transformations can cope with advanced features of service-oriented computing, such as several logical notions of scoping together with the interplay between linking and containment. We first exploit a graph algebra and set up a graph model that supports graph transformations in the DPO approach. Then, we show how to represent CaSPiS processes as hierarchical graphs in the graph model and their behaviors as graph transformation rules. Finally, we provide the soundness and completeness results of these rules with respect to the reduction semantics of CaSPiS.     

Self-organizing maps for learning the edit costs in graph matching.

Although graph matching and graph edit distance computation have become areas of intensive research recently, the automatic inference of the cost of edit operations has remained an open problem. In the present paper, we address the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs. We propose a system of self-organizing maps (SOMs) that represent the distance measuring spaces of node and edge labels. Our learning process is based on the concept of self-organization. It adapts the edit costs in such a way that the similarity of graphs from the same class is increased, whereas the similarity of graphs from different classes decreases. The learning procedure is demonstrated on two different applications involving line drawing graphs and graphs representing diatoms, respectively.     

基于层次图变换的多Agent组织结构动态重组机制

如何动态适应环境是基于组织计算的多Agent系统的关键研究内容之一.组织结构的动态重组为多Agent系统柔性地实现组织目标提供了有效途径.结合Agent组织结构特点,给出了一种描述组织结构的社会结构、角色指定和Agent协调的单根节点层次图模型.通过单根节点和层次化地维护组织结构内元素的拓扑关系,有效地降低了大规模Agent组织重组问题的复杂性;扩展DPO(double-pushout)代数图变换,形式定义了Agent组织结构的重组过程.单根节点层次图描述了重组过程中给定时刻的组织结构状态,图变换规则序列定义了组织结构的变化过程.Agent组织重组和图匹配算法实验结果表明,该层次图变换方法有效地刻画了多Agent组织动态重组过程,并支持图形化重组过程要素设计和大规模Agent组织的重组计算.     

TreeNetViz: revealing patterns of networks over tree structures

Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns.     

Synthesis of mutual exclusion solutions based on binary semaphores

A graphical form of the mutual exclusion problem is considered in which each vertex represents a process and each edge represents a mutual exclusion constraint between the critical sections of the processes associated with its endpoints. An edge semaphore solution for mutual exclusion problems is defined, and those graphs which are edge solvable are characterized in terms of both a forbidden subgraph and a graph grammar. Finally, an efficient algorithm is given which generates the entry and exit sections for all processes in an edge-solvable problem     

图网络层级信息挖掘分类算法综述

深度学习作为人工智能的一个研究分支发展迅速,而研究数据主要是语音、图像和视频等,这些具有规则结构的数据通常在欧氏空间中表示。然而许多学习任务需要处理的数据是从非欧氏空间中生成,这些数据特征和其关系结构可以用图来定义。图卷积神经网络通过将卷积定理应用于图,完成节点之间的信息传播与聚合,成为建模图数据一种有效的方法。尽管图卷积神经网络取得了巨大成功,但针对图任务中的节点分类问题,由于深层图结构优化的特有难点——过平滑现象,现有的多数模型都只有两三层的浅层模型架构。在理论上,图卷积神经网络的深层结构可以获得更多节点表征信息,因此针对其层级信息进行研究,将层级结构算法迁移到图数据分析的核心在于图层级卷积算子构建和图层级间信息融合。本文对图网络层级信息挖掘算法进行综述,介绍图神经网络的发展背景、存在问题以及图卷积神经网络层级结构算法的发展,根据不同图卷积层级信息处理将现有算法分为正则化方法和架构调整方法。正则化方法通过重新构建图卷积算子更好地聚合邻域信息,而架构调整方法则融合层级信息丰富节点表征。图卷积神经网络层级特性实验表明,图结构中存在层级特性节点,现有图层级信息挖掘算法仍未对层级特性节点的图信息进行完全探索。最后,总结了图卷积神经网络层级信息挖掘模型的主要应用领域,并从计算效率、大规模数据、动态图和应用场景等方面提出进一步研究的方向。     

Force-Directed Edge Bundling for Graph Visualization

Graphs depicted as node-link diagrams are widely used to show relationships between entities. However, node-link diagrams comprised of a large number of nodes and edges often suffer from visual clutter. The use of edge bundling remedies this and reveals high-level edge patterns. Previous methods require the graph to contain a hierarchy for this, or they construct a control mesh to guide the edge bundling process, which often results in bundles that show considerable variation in curvature along the overall bundle direction. We present a new edge bundling method that uses a self-organizing approach to bundling in which edges are modeled as flexible springs that can attract each other. In contrast to previous methods, no hierarchy is used and no control mesh. The resulting bundled graphs show significant clutter reduction and clearly visible high-level edge patterns. Curvature variation is furthermore minimized, resulting in smooth bundles that are easy to follow. Finally, we present a rendering technique that can be used to emphasize the bundling.     

On aggregation in multiset-based self-assembly of graphs

We continue the formal study of multiset-based self-assembly. The process of self-assembly of graphs, where iteratively new nodes are attached to a given graph, is guided by rules operating on nodes labelled by multisets. In this way, the multisets and rules model connection points (such as “sticky ends”) and complementarity/affinity between connection points, respectively. We identify three natural ways (individual, free, and collective) to attach (aggregate) new nodes to the graph, and study the generative power of the corresponding self-assembly systems. For example, it turns out that individual aggregation can be simulated by free or collective aggregation. However, we demonstrate that, for a fixed set of connection points, collective aggregation is rather restrictive. We also give a number of results that are independent of the way that aggregation is performed.