一种基于社团结构的局域复杂网络模型

BA模型用增长和优先连接两个机制解释了复杂网络的基本特性幂律分布,局域世界模型通过注意到优先连接是限制性的而进行了进一步的发展,本文认为局域世界模型中局部集团中的节点事实上是有着密切关系的,因而在新节点加入时采用GNM算法进行社团分解产生局部集团,提出基于社团分解的局域复杂网络模型(CLW模型).我们进行的理论分析和实验模拟表明,CLW模型具有小的网络平均最短路径,同时它的平均聚类系数要远大于局域世界模型,更接近于真实的复杂网络.     

复杂网络中重要性节点发掘综述

发掘网络中重要性^1节点（边）一直是图论领域的一个基本问题。随着近年来复杂网络研究热潮的兴起，特别是很多实际网络所抽象出来的复杂网络，表现出了与以往图理论不同的特性，如小世界特性、无尺度特性等。如何在复杂网络环境下，发掘重要性节点已经成为复杂网络研究的一个基本问题。本文简要介绍了复杂网络的基本概念，详细总结、分析了在复杂网络环境下几个领域中发掘重要性节点的方法，最后提出了这一领域内几个有待深入研究的问题和可能的应用方向。     

复杂网络理论研究综述

当前,复杂网络已迅速形成了一门贯穿多领域的交叉性学科,其相关理论被应用于诸多领域.为了解复杂网络的研究现状,首先从复杂网络的定义与统计特性两个角度介绍了复杂网络的基本概念,然后列举了几种典型的复杂网络模型,以及在此基础上对其进行改进后所建立的模型并讨论其优缺点,围绕复杂网络结构特性与网络动力学两个方面进一步分析了当前复杂网络的研究现状并列举了近几年的研究成果,最后得出结论并对复杂网络未来的热点研究方向做出展望.     

基于网络方法的DNA序列编码区·非编码区性质研究

利用了一种基于图论理论的方法对DNA序列(片段),其编码区及非编码区进行分析。该方法通过复杂网络研究生物体的拓扑结构,主要通过测量聚类系数(也可称:集团系数)构建网络的拓扑结构。依据DNA序列的前缀、后缀关联性质构造了所选取DNA序列(片段),其编码区和非编码区的相关网络,发现以上网络分布满足幂率特征,有较大的聚类系数(集团系数)。结果表明构建得到的网络同时满足小世界网络和无尺度网络的特征,证明DNA序列不全是随机的序列,而是有随机扰动的确定结构的序列,特别是编码区。     

中国北车集团计算机网络安全系统的设计与实现

中国北车集团计算机网络系统既有局域网也有广域网，网络覆盖规模大，体系结构复杂，且高速广域网采用虚拟专网（VPN）技术以国际互连网（Internet）作为载体。Internet网络病毒的泛滥、内外部用户的恶意攻击、关键应用系统的数据安全等已成为影响集团网络系统安全稳定运行的主要因素。为了保证集团网络系统安全可靠运行，集团逐步建立了计算机网络安全系统．重点解决网络用户行为管理、网络病毒的防杀、网络系统管理和关键应用系统的数据备份．     

矿业集团企业信息化协同机制研究

矿业集团是国有大中型企业,为国民经济的快速稳步发展提供能源支持,其信息化建设较为系统和复杂。该文对信息化过程中协同机制的需求与挑战进行了分析,给出了应用计算机网络和编程技术,将安全监控、资源计划、在线办公等模块进行信息化协同的方法和途径。     

基于系统观的网络突现性研究

因特网作为一个复杂适应系统，呈现了许多突现性。本文重点提出因特网中的两个突现现象：网络流量呈现自相似以及拓扑结构呈Power-law分布，并对其现象及其形成机制进行了初步探讨；同时，对网络上业务量的自组织临界性进行了简单的分析，从而提出用复杂科学理论来研究探索网络复杂性，对研究和开发下一代网络体系结构具有积极作
用和影响。     

基于模块度优化的蛋白质网络集团探测与分析

探测蛋白质相互作用网络中的功能模块对于理解生物系统的组织和功能具有重要的意义。目前,普遍的做法是将蛋白质相互作用网络表示成一个图,利用各种图聚类算法来挖掘功能模块。本文采用了基于模块度优化的图聚类算法来探测蛋白质相互作用网络中的集团,从具有2617个节点11855个相互作用的酵母蛋白相互作用网络中探测出68个集团。对于得到的集团,首先从拓扑结构的角度验证其的确是内部连接稠密的子图,然后分析了MIPS数据库中ComplexCat提供的已知的蛋白质复合体与这些集团的重叠情况,发现很多蛋白质复合体完全包含在某些集团中,最后使用超几何聚集分布的P值来分析一个集团对某个特定功能的富集程度,并根据最小的P值对应的功能来注释该集团的主要功能,发现集团中大部分的蛋白质具有相同的功能。研究结果表明,该方法探测的集团具有重要的生物学功能意义。     

干扰条件下城市交通网络系统仿真

建立城市交通复杂网络模型，遴选表征网络拓扑结构和功能的参数，系统设计多种表达真实网络抗干扰的仿真策略，以上海市轨道交通网络为例模拟分析其抗干扰特性，提供了分析和优化交通基础设施结构和功能的模型和思路。     

银行账户交易复杂网络特性分析

首先基于银行账户交易的特点，建立了一个有向加权的银行账户交易网络通用模型。进而，根据复杂网络的定义，从网络结构和节点2个层面，验证了交易网络的复杂网络特性。其中，网络结构特性包括静态特性（即无标度特性和小世界特性）和动态演化特性（即自组织特性）。此外，使用吸引子特性验证了复杂网络节点的动力学特征。分析包含非法传销交易的真实银行交易数据，得出如下结论：该网络具有无标度特性、小世界特性、部分自组织特性和奇异吸引子。     

ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS

Community discovery in complex networks is the problem of detecting, for each node of the network, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive, or, more in general, similar, according to a similarity function. So far, the problem has been widely studied in monodimensional networks, i.e. networks where only one connection between two entities may exist. However, real networks are often multidimensional, i.e., multiple connections between any two nodes may exist, either reflecting different kinds of relationships, or representing different values of the same type of tie. In this context, the problem of community discovery has to be redefined, taking into account multidimensional structure of the graph. We define a new concept of community that groups together nodes sharing memberships to the same monodimensional communities in the different single dimensions. As we show, such communities are meaningful and able to group nodes even if they might not be connected in any of the monodimensional networks. We devise frequent pAttern mining-BAsed Community discoverer in mUltidimensional networkS (ABACUS), an algorithm that is able to extract multidimensional communities based on the extraction of frequent closed itemsets from monodimensional community memberships. Experiments on two different real multidimensional networks confirm the meaningfulness of the introduced concepts, and open the way for a new class of algorithms for community discovery that do not rely on the dense connections among nodes.     

基于随机网络集成模型的广义网络社区挖掘算法

根据结点的属性和链接关系,现实世界中的复杂网络大多可分为同配网络和异配网络,社区结构在这两类网络中均普遍存在. 准确地挖掘出两种不同类型网络的社区结构具有重要的理论意义和广泛的应用领域.由于待处理的网络类型通常未知, 因而难以事先确定应当选择何种类型的网络社区挖掘算法才能获得有意义的社区结构. 针对该问题, 本文提出了广义网络社区概念,力图将同配和异配网络社区结构统一起来. 本文提出了随机网络集成模型, 进而提出了广义网络社区挖掘算法G-NCMA. 实验结果表明: 该算法能够在网络类型未知的前提下准确地挖掘出有意义的社区结构, 并能分析出所得社区的类型特征.     

Dual modularity optimization for detecting overlapping communities in bipartite networks

Many algorithms have been designed to discover community structure in networks. These algorithms are mostly dedicated to detecting disjoint communities. Very few of them are intended to discover overlapping communities, particularly the bipartite networks have hardly been explored for the detection of such communities. In this paper, we describe a new approach which consists in forming overlapping mixed communities in a bipartite network based on dual optimization of modularity. To this end, we propose two algorithms. The first one is an evolutionary algorithm dedicated for global optimization of the Newman’s modularity on the line graph. This algorithm has been tested on well-known real benchmark networks and compared with several other existing methods of community detection in networks. The second one is an algorithm that locally optimizes the graph Mancoridis modularity, and we have adapted to a bipartite graph. Specifically, this second algorithm is applied to the decomposition of vertices, resulting from the evolutionary process, and also characterizes the overlapping communities taking into account their semantic aspect. Our approach requires a priori no knowledge on the number of communities searched in the network. We show its interest on two datasets, namely, a group of synthetic networks and real-world network whose structure is also difficult to understand.     

Community detection in national-scale high voltage transmission networks using genetic algorithms

The large-scale interconnection of electricity networks has been one of the most important investments made by electric companies, and this trend is expected to continue in the future. One of the research topics in this field is the application of graph-based analysis to identify the characteristics of power grids. In particular, the application of community detection techniques allows for the identification of network elements that share valuable properties by partitioning a network into some loosely coupled sub-networks (communities) of similar scale, such that nodes within a community are densely linked, while connections between different communities are sparser. This paper proposes the use of competitive genetic algorithms to rapidly detect any number of community structures in complex grid networks. Results obtained in several national- scale high voltage transmission networks, including Italy, Germany, France, the Iberian peninsula (Spain and Portugal), Texas (US), and the IEEE 118 bus test case that represents a portion of the American Electric Power System (in the Midwestern US), show the good performance of genetic algorithms to detect communities in power grids. In addition to the topological analysis of power grids, the implications of these results from an engineering point of view are discussed, as well as how they could be used to analyze the vulnerability risk of power grids to avoid large-scale cascade failures.     

Multidimensional Projections for Visual Analysis of Social Networks

Visual analysis of social networks is usually based on graph drawing algorithms and tools.However,social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context.Context,in its turn,is given by attributes associated with graph elements,such as individual nodes,edges,and groups of edges,as well as by the nature of the connections between individuals.In most systems,attributes of individuals and communities are not taken into consideration during graph layout,except to derive weights for force-based placement strategies.This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings.These properties are employed to layout nodes on the plane via multidimensional projection techniques.For the attribute mapping,we show that node proximity in the layout corresponds to similarity in attribute,leading to easiness in locating similar groups of nodes.The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm,reaching a meaningful layout in one pass.When a force algorithm is then applied to this initial mapping,the final layout presents better properties than conventional force-based approaches.Numerical evaluations show a number of advantages of pre-mapping points via projections.User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone.In order to allow better space usage for complex networks,a graph mapping on the surface of a sphere is also implemented.     

A Heuristic Clustering Algorithm for Mining Communities in Signed Networks

Signed network is an important kind of complex network, which includes both positive relations and negative relations. Communities of a signed network are defined as the groups of vertices, within which positive relations are dense and between which negative relations are also dense. Being able to identify communities of signed networks is helpful for analysis of such networks. Hitherto many algorithms for detecting network communities have been developed. However, most of them are designed exclusively for the networks including only positive relations and are not suitable for signed networks. So the problem of mining communities of signed networks quickly and correctly has not been solved satisfactorily. In this paper, we propose a heuristic algorithm to address this issue. Compared with major existing methods, our approach has three distinct features. First, it is very fast with a roughly linear time with respect to network size. Second, it exhibits a good clustering capability and especially can work well with complex networks without well-defined community structures. Finally, it is insensitive to its built-in parameters and requires no prior knowledge.     

Finding overlapping communities in a complex network of social linkages and Internet of things

Online social networks play an important role in today’s Internet. These social networks contain huge amounts of data and the integrated framework of SN with Internet of things (IoT) presents a challenging problem. IoT is the ubiquitous interconnection of everyday items of interest (things), providing connectivity anytime, anywhere, and with anything. Like biological, co-authorship, and virus-spread networks, IoT and Social Network (SN) can be characterized to be complex networks containing substantial useful information. In the past few years, community detection in graphs has been an active area of research (Lee and Won in Proceedings of IEEE SoutheastCon, pp. 1–5, 2012). Many graph mining algorithms have been proposed, but none of them can help in capturing an important dimension of SNs, which is friendship. A friend circle expands with the help of mutual friends, and, thus, mutual friends play an important role in social networks’ growth. We propose two graph clustering algorithms: one for undirected graphs such as Facebook and Google+, and the other for directed graphs such as Twitter. The algorithms extract communities, and based on the access control policy nodes share resources (things). In the proposed Community Detection in Integrated IoT and SN (CDIISN) algorithm, we divide the nodes/actors of complex networks into basic, and IoT nodes. We, then, execute the community detection algorithm on them. We take nodes of a graph as members of a SN, and edges depicting the relations between the nodes. The CDIISN algorithm is purely deterministic, and no fuzzy communities are formed. It is known that one community detection algorithm is not suitable for all types of networks. For different network structures, different algorithms exhibit different results, and methods of execution. However, in our proposed method, the community detection algorithm can be modified as desired by a user based on the network connections. The proposed community detection approach is unique in the sense that a user can define his community detection criteria based on the kind of network.     

基于DNA遗传算法的复杂网络社区结构发现

复杂网络社区结构划分日益成为近年来复杂网络的研究热点,到目前为止,已经提出了很多分析复杂网络社区结构的算法。但是大部分算法还存在一定的缺陷,而且有些算法由于其时间复杂度的过高导致其不合适应用于对大型网络的分析。提出了一种基于DNA遗传算法的复杂网络社区结构分析的方法。该方法无须预先知道社区内结点的数量以及任何门限值。该算法的可行性用Zachary Karate Club和College Football Network模型进行验证。     

Blocking of group automata. I. One-sided interlocking

The concepts of restraining and interlocking connections of the set of automata are introduced. These concepts are used to define the concept of commutation automaton which serves as the mathematical model of switching dynamics in electrical networks regulated by the rules of switching technology. One-sided and mutual automata interlockings are determined. The structure of groups of one-sided interlocking connections of permutation automata is studied.     

Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms

In order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big Bang–Big Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks − five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested.