基于PSO微粒群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适合应用于对大型网络的分析。提出了一种基于PSO微粒群算法的复杂网络社区结构分析方法。此方法无需预先知道组成该复杂网络的社区数量、社区内的节点数以及任何门限值。该算法的可行性用Zachary Karate Club和College Football Network模型进行验证。     

一种基于聚集系数的社区发现算法

社区划分一直是复杂网络研究中的一个热门话题,社区的快速准确划分为研究复杂网络的性质提供了良好的基础。传统的社区发现方法都是在全局复杂网络的基础上进行社区划分,随着网络中节点的增加,网络规模的变大,社区发现变得更为复杂。提出了一种局部社区发现算法,该算法无需知道整个复杂网络的全部信息,只需从一个待求节点出发,考察其与邻接节点的紧密程度,逐步将邻接点添加到社区中,得到该节点所在的社区结构。同时,该算法还可实现全局网络的社区发现。利用该算法分别对Zachary空手道俱乐部网络和海豚社会网络进行社区发现,实验结果表明了该算法的准确性与可行性。     

基于改进的PSO算法的网络社区划分方法

网络社区划分是复杂网络研究领域的一个热点,现有的复杂网络社区划分方法时间复杂度比较高,准确性过于依赖先验知识,因此许多现有的社区划分方法不太适用于实际网络的社区结构分析。对PSO算法进行改进,改进后的PSO算法的参数设置更简单。基于改进后的PSO算法,提出一种复杂网络社区划分方法,该社区划分方法时间复杂度比较低,并且无需预先知道网络的社区数量、社区节点数。实验结果表明该方法具有良好的性能。     

复杂网络局部社区挖掘的节点接近度算法

为了准确、快速地发现大规模复杂网络中的局部社区,提出了一种基于节点接近度的局部社区发现算法。该算法以最大度节点作为起始节点,利用节点接近度和局部社区Q值不断搜索其邻居节点,将接近度最大的节点加入初始社区形成新的初始社区;同时,该算法也可以应用于复杂网络全局社区结构的划分。对2个典型复杂网络进行了局部社区挖掘分析,实验结果表明,该算法能够有效识别隐藏在实验网络中的局部社区。针对稀疏网络,该算法的时间复杂度为O（nlog（n））,n为网络节点数。     

基于GPU的复杂网络社区挖掘算法并行计算

由于复杂网络的规模越来越大, 在大规模的复杂网络中快速、准确地挖掘出隐藏的社区结构是当前该领域研究的热点问题。目前社区结构挖掘常用的基于快速Newman算法的社区结构挖掘算法之一是一般概率框架方法。以规模日益增大的复杂网络为研究对象, 提出了基于GPGPU的一般概率框架并行算法, 有效地解决了在大规模的复杂网络中快速、准确地挖掘出隐藏的社区结构问题。实验证明, 随着节点数的增加, 该并行算法在不损失准确性的前提下运行效率有所提高, 为复杂网络社区结构挖掘的研究提供了一种高效的解决方案。     

用于网络重叠社区发现的粗糙谱聚类算法

针对绝大多数社区发现算法都存在着网络节点仅隶属于一个社区的假设,引入谱图理论与粗糙集理论来分析复杂网络社区,提出一种用于网络重叠社区发现的粗糙谱聚类算法RSC,该算法用上下近似来刻画网络节点的社区归属,边界表示社区之间共享的节点,通过优化重叠社区结构模块度来实现重叠社区发现.通过3个不同类型真实网络的仿真实验,结果验证了该方法的可行性与有效性.     

基于社区结构的复杂网络鲁棒性优化策略

为在复杂网络鲁棒性优化过程中尽可能保留网络初始社区结构,分析重连边策略对网络社区结构的影响,提出一种结合社区结构的复杂网络鲁棒性优化策略。采用Louvain算法确定复杂网络社区结构,利用模拟退火算法提升复杂网络中单个社区的内部鲁棒性,使用改进的智能重连边策略（Smart Rewiring）提升社区间的连接鲁棒性,并通过标准化互信息指标评价鲁棒性优化过程中社区结构的保留程度。在BA、WS和WU-PowerGrid网络中的实验结果表明,与Smart Rewiring和MA策略相比,该策略能在提升网络鲁棒性的同时尽可能保留网络初始社区结构。     

基于最大生成树的重叠社区发现算法

挖掘复杂网络的重叠社区结构对研究复杂系统具有重要的理论和实践意义。针对局部扩展算法(local fitness method,LFM)随机选取种子节点造成的社区结果鲁棒性较低等问题,提出了一种基于最大生成树的重叠社区发现算法:提出一种新颖的边权重定义,将无权的网络转换为带权重的网络,而且该权重真实反映了网络真实结构;提出一种节点影响力计算方法,反映节点在整个网络结构中的重要程度;提出了一种新的生成候选种子集的方法,并借助最大生成树使得到的候选种子节点在网络中更具有代表性;对初始社区划分结果进行优化,避免社区之间重叠度过多。经仿真实验发现,该算法与经典的重叠社区发现算法相比,无论在真实网络还是LFR人工网络上,均有良好的表现。     

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.     

一种基于三角环的社区发现算法

在大型复杂网络中自动搜寻或发现社区具有重要的实际应用价值。该文把超图模型以及基于此的聚类算法应用到社区结构发现领域。对于简单图的社区发现,引入了边凝聚系数和三角环等概念,提出了基于三角环的社区结构发现方法。通过Zachary网络的实例验证和算法的对比分析,证明了该算法在时间复杂度上能提高一个数量级。     

基于局部相似性的复杂网络社区发现方法

复杂网络是复杂系统的典型表现形式, 社区结构是复杂网络最重要的结构特征之一. 针对复杂网络的社区结构发现问题, 本文提出一种新的局部相似性度量, 并结合层次聚类算法用于社区结构发现. 相对全局的相似性度量, 本文提出的相似性度量具有较低的计算开销; 同时又能很好地刻画网络的结构特征, 克服了传统局部相似性度量在某些情形下对节点相似性的低估倾向. 为了将局部相似性度量用于社区结构发现, 推广了传统的Ward层次聚类算法, 使之适用于具有相似性度量的任意对象, 并将其用于复杂网络社区结构发现. 在合成和真实世界的网络上进行了实验, 并与典型算法进行了比较, 实验结果表明所提算法的可行性和有效性.     

基于最短路径和最大模块度融合的社团发现算法

复杂网络已成为当前的一个研究热点,复杂网络具有许多重要性质,其中社团结构是复杂网络最普遍最重要的拓扑性质之一。目前已有很多流行的网络社团挖掘算法,但是大部分社团挖掘算法存在准确性低、适用范围窄等缺陷,为了克服这些缺点,本文结合社团挖掘的相关研究,提出一种基于改进近邻传播的社团挖掘算法。首先采用最短路径计算任意节点对之间的距离,并运用近邻传播算法初步识别中心点;然后基于模块度优化的思想,建立“中心点过滤”数学模型,自动识别网络的社团结构;最后对本算法在一些广泛使用的网络数据上进行性能测试。测试结果表明,本算法与传统方法相比,具有适用范围广、准确率高、容忍分辨极限能力强等优点。     

Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network

Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman＇s algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.     

Multi-level learning based memetic algorithm for community detection

Complex network has become an important way to analyze the massive disordered information of complex systems, and its community structure property is indispensable to discover the potential functionality of these systems. The research on uncovering the community structure of networks has attracted great attentions from various fields in recent years. Many community detection approaches have been proposed based on the modularity optimization. Among them, the algorithms which optimize one initial solution to a better one are easy to get into local optima. Moreover, the algorithms which are susceptible to the optimized order are easy to obtain unstable solutions. In addition, the algorithms which simultaneously optimize a population of solutions have high computational complexity, and thus they are difficult to apply to practical problems. To solve the above problems, in this study, we propose a fast memetic algorithm with multi-level learning strategies for community detection by optimizing modularity. The proposed algorithm adopts genetic algorithm to optimize a population of solutions and uses the proposed multi-level learning strategies to accelerate the optimization process. The multi-level learning strategies are devised based on the potential knowledge of the node, community and partition structures of networks, and they work on the network at nodes, communities and network partitions levels, respectively. Extensive experiments on both benchmarks and real-world networks demonstrate that compared with the state-of-the-art community detection algorithms, the proposed algorithm has effective performance on discovering the community structure of networks.     

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.     

An improved algorithm for finding community structure in networks with an application to IPv6 backbone network

The discovery of community structure in a large number of complex networks has attracted lots of interest in recent years. One category of algorithms for detecting community structure, the divisive algorithms, has been proposed and improved impressively. In this paper, we propose an improved divisive algorithm, the basic idea of which is to take more than one parameters into consideration to describe the networks from different points of view. Although its basic idea appears to be a little simple, it is shown experimentally that it outperforms some other algorithms when it is applied to the networks with a relatively obscure community structure. We also demonstrate its effectiveness by applying it to IPv6 backbone network. The communities detected by our algorithm indicate that although underdeveloped compared with IPv4 network, IPv6 network has already exhibited a preliminary community structure. Moreover, our algorithm can be further extended and adapted in the future. In fact, it suggests a simple yet possibly efficient way to improve algorithms.     

Detecting community structure in complex networks using genetic algorithm based on object migrating automata

Community structure is an important topological feature of complex networks. Detecting community structure is a highly challenging problem in analyzing complex networks and has great importance in understanding the function and organization of networks. Up until now, numerous algorithms have been proposed for detecting community structure in complex networks. A wide range of these algorithms use the maximization of a quality function called modularity. In this article, three different algorithms, namely, MEM-net, OMA-net, and GAOMA-net, have been proposed for detecting community structure in complex networks. In GAOMA-net algorithm, which is the main proposed algorithm of this article, the combination of genetic algorithm (GA) and object migrating automata (OMA) has been used. In GAOMA-net algorithm, the MEM-net algorithm has been used as a heuristic to generate a portion of the initial population. The experiments on both real-world and synthetic benchmark networks indicate that GAOMA-net algorithm is efficient for detecting community structure in complex networks.     

多层网络的结构与功能研究进展

社区发现旨在挖掘复杂网络蕴含的社区结构,是复杂网络分析的重要任务之一. 然而,现有的社区发现方法大多针对单层网络数据,对现实世界中广泛存在的多层网络数据的研究较少. 针对多层网络的社区发现问题,提出了一个基于2阶段集成的社区发现算法,以提高社区发现结果的准确性和可解释性. 首先,在各层分别得到基社区划分;其次以各层社区划分结构信息为主并结合其他各层网络得到的基社区划分中最优的社区划分信息进行局部集成;再次,基于信息熵对各层局部社区划分中各个社区的稳定性进行度量,并通过其他层社区划分结果来对各个局部社区划分的准确性进行评价;最后,基于各个社区以及社区划分的重要性进行全局加权集成得到最终的社区划分结果. 在人造多层网络和真实多层网络数据上与已有的多层网络社区发现算法进行了比较分析. 实验结果表明,提出的算法在多层模块度、标准化互信息等评价指标上优于已有算法.