基于节点间依赖度的社团结构划分方法

本文提出了一种基于节点间依赖度的在复杂网络中划分社团结构的算法,定义了节点对其邻居的依赖度以及节点对社团的依赖度和条件依赖度.算法的基本要点是优先将最大依赖度不小于其他节点且有惟一依赖节点的节点划分到社团,并将对社团的依赖度或条件依赖度达到一定值的节点吸收进社团,直到所有节点都得到准确的社团划分.本算法在几个实际网络的测试上,都成功地划分出了满足条件的社团,并且对社团结构已知的网络的划分结果符合实际情况.     

加权网络权重自相似评判函数及其社团结构检测

提出了权重自相似性加权网络社团结构评判函数,并基于该函数提出一种谱分析算法检测社团结构,结果表明算法能将加权网络划分为同一社团内边权值分布均匀,而社团间边权值分布随机的社团结构.通过建立具有社团结构的加权随机网络分析了该算法的准确性,与WEO和WGN算法相比,在评判权重自相似的阈值系数取较小时,该算法具有较高的准确性.对于一个具有n个节点和c个社团的加权网络,社团结构检测的复杂度为O(cn2/2).通过设置评判权重自相似的阈值系数,可检测出能反映节点联系稳定性的层化性社团结构.这与传统意义上只将加权网络划分为社团中边权值较大而社团间边权值较小的标准不同,从另一个角度更好地提取了加权网络的结构信息.     

基于区块链的混合式点对点电力信息网络级联故障研究

通过对电网进行社团划分,根据电网和信息网对应分层分区建设的现状和实际耦合关系划分信息网社团。选取信息网中每个社团介数最大的节点和度数最大的节点进行全连接,构建基于区块链的混合式点对点结构的电力信息相互依存网络模型。结合区块链的共识机制分析该模型的优势,利用高度数攻击与高介数攻击这两种攻击策略研究所提出的相互依存网络的鲁棒性,并与传统的集中式控制的电力信息网和完全分散式点对点电力信息网进行对比。研究结果表明：本电力信息相互依存网络模型能够在提高网络鲁棒性的基础上,有效减少实用拜占庭容错算法的通信开销和共识时延;在混合式点对点电力信息网中,高度数攻击方式下系统表现出更强的脆弱性。     

一种新的网络传播中最有影响力的节点发现方法

在复杂网络的传播模型研究中, 如何发现最具影响力的传播节点在理论和现实应用中都有重大的意义. 目前的研究一般使用节点的度数、紧密度、介数和K-shell等中心化指标来评价影响力, 这种方法虽然简单, 但是由于它们仅利用了节点自身的内部属性, 因而在评价影响力时精确度并不高, 普遍性适用性较弱.为了解决这个问题, 本文提出了KSC (K-shell and community centrality)指标模型. 此模型不但考虑了节点的内部属性, 而且还综合考虑了节点的外部属性, 例如节点所属的社区等. 然后利用SIR (susceptible-infected-recovered)模型对传播过程进行仿真, 实验证明所提出的方法可以更好地发现最具有影响力的节点, 且可适用于各种复杂网络. 本文为这项具有挑战性研究提供了新的思想和方法. 关键词： 复杂网络 最具影响力的节点 社区划分 中性化测量     

基于子网划分的电网关键节点识别

结合电网拓扑结构和潮流追踪技术,提出一种基于子网划分的电网关键节点识别方法。首先,根据发电机节点的邻域信息和功率将发电机节点划分为不同的子集,然后根据电网的系数分配矩阵将负荷节点划分到为其提供最大功率的发电机节点子集中,完成子网划分。接着采用多属性决策法对每个子网的节点进行排序,进一步改进并计算每个子网的结构系数,作为衡量子网重要性的指标。根据子网重要性,从每个子网中提取特定比例的候选关键节点,对这些候选节点依据多属性决策法重新排序,得到关键节点的最终排序。以IEEE14、IEEE57和IEEE118三种节点系统为例进行分析,得到各个系统的子网划分结果和各个标准网络的重要节点排序结果。采用本文方法、PageRank法和多属性决策法分别进行关键节点排序,并对排序靠前的关键节点进行级联故障性能实验和网络效能实验。实验表明,本文算法选择的关键节点对整个网络的传播性能影响最大,优于其他两种关键节点识别方法。     

基于社团结构的负载传输优化策略研究

研究表明网络社团结构特征对负载传输有影响,明显社团结构特征会降低网络的承载能力.由于最短路由策略在选择路由时有一定的随机性,本文提出了一种基于社团结构的负载传输策略,减少最短路由经过的社团数量,从而降低社团边缘节点的介数.实验结果显示,该策略在保证最短路由小世界特性的同时,提升了网络的承载能力,社团划分得越准确传输优化策略效果越显著. 关键词： 优化路由策略 社团结构 复杂网络 负载传输     

加权复杂网络社团的评价指标及其发现算法分析

节点的聚集现象是复杂网络的重要特性.以往研究主要发现无权复杂网络中的社团,较少涉及加权网络的社团发现.由于加权网络的复杂性远高于无权网络,一般认为加权网络的社团发现是一个较难的问题.本文基于统一的数据基础,从社团评价指标的有效性和现有算法的效果两个角度开展研究.首先,总结了加权网络三种常见的社团评估指标,并在社团大小、密度和局域特点均不同的模拟数据集上分析指标的有效性;其次,针对5个数据集,分析现有的3种加权复杂网络社团发现算法的效果.研究表明:上述指标无论在评价最基本的社团结构,还是在分析结构复杂的社团时都有较大缺欠;现有的加权网络社团发现算法的泛化能力不强.     

网络的模块矩阵及其社团结构指标

探讨了复杂网络的模块矩阵的正(负)特征谱与网络的社团结构(反社团结构)的关系,给出了反映网络社团结构性质的相关定义.利用模块矩阵的多个特征值与特征向量,引入反映个体对所处社团的依附程度一种结构中心化指标.利用人工网络与实际网络数据,将这种指标与几种经典的中心化指标进行了比较.结果表明该指标具有较好的分辨率并与度指标具有一定程度的相关性.     

交织型层级复杂网

为研究两个异质关联网络复合后的结构特征与节点中心性特征,本文提出了交织型层级复杂网络的概念,可描述由两个具有部分相同节点,连接边属性近似的子网所构成的层级复杂网络,并定义了节点交织系数、路径交织系数和网络交织系数3种测度用于衡量两个子网之间的密切程度.针对该类网络,研究并改进了节点度中心性和介数中心性的计算方法,同时提出一种新的中心性指标—助联性,用于衡量子网的某一节点对另一子网联通性和流通性的助益.通过实验分析,验证了本文各类指标的有效性.     

面向嵌入式平台的轻量级光学遥感图像舰船检测

针对现有基于深度学习的轻量级目标检测算法对复杂遥感场景图像中舰船目标检测精度低、检测速度慢的问题,提出了一种面向嵌入式平台的轻量级光学遥感图像舰船实时检测算法（STYOLO）。首先,针对主干网络内存访问成本较高的问题,利用高效网络架构ShuffleNet v2作为主干网络对图像进行特征提取,降低内存访问成本,提高网络并行度;其次,利用Slim-neck特征融合结构作为特征增强网络,以融合较低层级特征图中的细节信息,增强对小目标的特征响应,在多尺度信息融合区域施加坐标注意力机制,强化目标关注以提高较难样本检测以及抗背景干扰能力;最后,提出一种跨域迁移和域内迁移相结合的学习策略,减少源域与目标域的差异性,提升迁移学习效果。实验结果表明：基于光学遥感图像舰船检测公开数据集HRSC2016,与同类型快速检测算法YOLOv5s相比,所提算法的检测精度提高了2.7个百分点,参数量减少了61.77%,在嵌入式平台Jetson Nano上检测速度达到102.8 frame/s,能够有效实现对光学遥感图像中舰船目标的实时、准确检测。     

Community Detection in Semantic Networks: A Multi-View Approach

The semantic social network is a complex system composed of nodes, links, and documents. Traditional semantic social network community detection algorithms only analyze network data from a single view, and there is no effective representation of semantic features at diverse levels of granularity. This paper proposes a multi-view integration method for community detection in semantic social network. We develop a data feature matrix based on node similarity and extract semantic features from the views of word frequency, keyword, and topic, respectively. To maximize the mutual information of each view, we use the robustness of L21-norm and F-norm to construct an adaptive loss function. On this foundation, we construct an optimization expression to generate the unified graph matrix and output the community structure with multiple views. Experiments on real social networks and benchmark datasets reveal that in semantic information analysis, multi-view is considerably better than single-view, and the performance of multi-view community detection outperforms traditional methods and multi-view clustering algorithms.     

Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding

Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks, solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.     

利用邻域“结构洞”寻找社会网络中最具影响力节点

识别复杂网络中的关键节点对网络结构优化和鲁棒性增强具有十分重要的意义. 经典的关键节点测量方法在一定程度上能够辨识网络中影响力节点, 但存在一定局限性: 局部中心性测量方法仅考虑节点邻居的数目, 忽略了邻居间的拓扑关系, 不能在计算中反映邻居节点间的相互作用; 全局测量方法则由于算法本身的复杂性而不能应用于大规模社会网络的分析, 另外, 经典的关键节点测量方法也没有考虑社会网络特有的社区特征. 为高效、准确地辨识具有社区结构的社会网络中最具影响力节点, 提出了一种基于节点及其邻域结构洞的局部中心性测量方法, 该方法综合考虑了节点的邻居数量及其与邻居间的拓扑结构, 在节点约束系数的计算中同时体现了节点的度属性和“桥接”属性. 利用SIR(易感-感染-免疫)模型在真实社会网络数据上对节点传播能力进行评价后发现, 所提方法可以准确地评价节点的传播能力且具有强的鲁棒性.     

Overlapping Community Detection Based on Attribute Augmented Graph

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.     

Adjusting from disjoint to overlapping community detection of complex networks

The investigation of community structures is one of the most important problems in the field of complex networks and has countless applications in different disciplines: biology, computer, social sciences, etc. Many community detection algorithms have been developed in various fields recently. The vast majority of these algorithms only find disjoint communities; however, in many real-world networks communities often overlap to some extent. In this paper, we propose an efficient method for adjusting these classical algorithms to match the requirement for discovering overlapping communities in complex networks, which is based on a local definition of community strength. The method can in principle be applied with any clustering algorithm. Tests on a set of computer generated and real-world networks give excellent results. In particular, we show that the method can also allow one to availably analyze the problem of unstable nodes in community detection, which is very helpful for understanding the structural properties of the networks correctly and comprehensively.     

Overlapping community detection using weighted consensus clustering

Many overlapping community detection algorithms have been proposed. Most of them are unstable and behave non-deterministically. In this paper, we use weighted consensus clustering for combining multiple base covers obtained by classic non-deterministic algorithms to improve the quality of the results. We first evaluate a reliability measure for each community in all base covers and assign a proportional weight to each one. Then we redefine the consensus matrix that takes into account not only the common membership of nodes, but also the reliability of the communities. Experimental results on both artificial and real-world networks show that our algorithm can find overlapping communities accurately.     

Research on Community Detection in Complex Networks Based on Internode Attraction

With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms.     

Refinement of the community detection performance by weighted relationship coupling

The complexity of many community detection algorithms is usually an exponential function with the scale which hard to uncover community structure with high speed. Inspired by the ideas of the famous modularity optimization, in this paper, we proposed a proper weighting scheme utilizing a novel k-strength relationship which naturally represents the coupling distance between two nodes. Community structure detection using a generalized weighted modularity measure is refined based on the weighted k-strength matrix. We apply our algorithm on both the famous benchmark network and the real networks. Theoretical analysis and experiments show that the weighted algorithm can uncover communities fast and accurately and can be easily extended to large-scale real networks.     

Graph spectra and the detectability of community structure in networks

We study networks that display community structure--groups of nodes within which connections are unusually dense. Using methods from random matrix theory, we calculate the spectra of such networks in the limit of large size, and hence demonstrate the presence of a phase transition in matrix methods for community detection, such as the popular modularity maximization method. The transition separates a regime in which such methods successfully detect the community structure from one in which the structure is present but is not detected. By comparing these results with recent analyses of maximum-likelihood methods, we are able to show that spectral modularity maximization is an optimal detection method in the sense that no other method will succeed in the regime where the modularity method fails.     

Ordered community structure in networks

Community structure in networks is often a consequence of homophily, or assortative mixing, based on some attribute of the vertices. For example, researchers may be grouped into communities corresponding to their research topic. This is possible if vertex attributes have unordered discrete values, but many networks exhibit assortative mixing by some ordered (discrete or continuous) attribute, such as age or geographical location. In such cases, the identification of discrete communities may be difficult or impossible. We consider how the notion of community structure can be generalized to networks that have assortative mixing by ordered attributes. We propose a method of generating synthetic networks with ordered communities and investigate the effect of ordered community structure on the spread of infectious diseases. We also show that current community detection algorithms fail to recover community structure in ordered networks, and evaluate an alternative method using a layout algorithm to recover the ordering.