基于局部信息的复杂网络社团结构发现算法

发现网络中的社团结构有助于更好地理解网络结构和分析网络属性。通过定义边的聚类系数和基于局部信息的方法,提出了一种寻找复杂网络中社团结构的算法。该算法首先在网络的剩余节点中寻找度最大的节点,然后利用该节点的局部信息、边的聚类系数和凝聚的思想,得到复杂网络的社团结构。在两个典型网络上的测试结果表明了该方法的可行性。     

基于节点相似度的社团发现算法

对现有的社会网络社团发现算法进行研究,发现存在算法时间复杂度高、准确率低和没有充分利用节点属性信息等问题,提出了一种基于节点相似度的社团发现算法以解决这些问题。综合考虑图的拓扑结构和节点属性信息,结合构造属性扩展图的思想和基于结构情境相似度的思想得到节点的相似度,利用改进的K-means算法对所有节点进行聚类得到社团结构。编程实验结果表明,使用该算法得到的社团准确率较高,算法的时间复杂度为线性的,在带属性的数据集上和不带属性的数据集上的测试结果均验证了算法的有效性。     

面向属性网络的重叠社区发现算法

针对现实世界的网络节点中包含大量属性信息并且社区之间呈现出重叠特性的问题,提出了一种面向属性网络的重叠社区发现算法。融合网络的拓扑结构和节点属性定义了节点的密集度和间隔度,分别用于描述社区内部连接紧密和外部连接松散的特点。基于密度峰值聚类的思想搜索局部密度中心作为社区中心,在此基础上给出了非中心节点关于各个社区的隶属度的迭代计算方法,实现了重叠社区的划分。在真实数据集上进行了仿真实验,实验结果表明所提算法相对于LINK、COPRA和DPSCD能获得更好的社区划分结果。     

复杂网络中基于节点相似性聚类的网络社团发现方法研究

针对复杂网络社团发现的问题,使用聚类方法对其进行详细的研究,将网络节点的数据结构转化成聚类算法的数据结构,根据节点之间的相似度对节点进行合并或分割,并且使用向量计算的方法对复杂网络的节点相似度进行度量。改进的算法把网络中的每个节点都作为一个信息源,具有收发信息的功能,按照改进的信息传递方法进行相似度值的传递和遍历,使用复杂网络中常用的Zachary俱乐部网络作为实验对象验证。本方法提高了复杂网络社团发现的算法效率。     

融合结构与属性相似性的加权图聚集算法

图聚集技术是将一个大规模图用简洁的小规模图来表示,同时保留原始图的结构和属性信息的技术。现有算法未同时考虑节点的属性信息与边的权重信息,导致图聚集后与原始图存在较大差异。因此,提出一种同时考虑节点属性信息与边权重信息的图聚集算法,使得聚集图既保留了节点属性相似度又保留了边权重信息。该算法首先定义了闭邻域结构相似度,通过一种剪枝策略来计算节点之间的结构相似度;其次使用最小哈希(MinHash)技术计算节点之间的属性相似度,并调节结构相似与属性相似所占的比例;最后,根据2方面相似度的大小对加权图进行聚集。实验表明了该算法可行且有效。     

基于节点拓扑结构和属性的重叠社区检测算法

近些年来,网络中的重叠社区检测应用越来越广泛,这极大引起了人们的兴趣。通常重叠社区检测只考虑节点的拓扑结构信息,忽略了节点的属性信息,这可能会遗漏数据间的重要结构。本文提出了一种基于节点拓扑结构和属性相似度的局部社区检测算法。首先,计算出节点与社区之间属性相似度。其次,为挖掘内部紧耦合的社区,计算出能够表示耦合性的模块度增量。最后,通过融合多个已检测到局部社区,计算出隶属矩阵从而获取全局重叠社区结构。在三个有真实社区的数据集上的实验结果从内部指标模块度和与真实社区的比较验证了基于节点拓扑结构和属性相似度的重叠社区检测算法比目前基于节点结构的社区检测算法在效率和有效性上有所提升。     

基于最大生成树的社团划分算法

针对层次聚类算法存在复杂度高、准确度低等问题,提出了一种基于最大生成树的社团划分算法。该算法重新定义了节点间相似度,并利用最大生成树进行初始聚类,然后根据社团相似度合并局部社团得到最终划分结果。算法不仅降低了时间复杂度,而且在划分社团的准确度方面有所提高。将该方法在真实网络与人工网络上进行验证和比对,实验结果表明基于最大生成树的社团划分算法能够快速、准确地划分出网络中的社团结构。     

基于Node2Vec的重叠社区发现算法

针对目前基于种子节点选择的社区发现算法在准确性和复杂度等方面存在的不足, 提出了一种基于Node2Vec的重叠社区发现算法. 首先, 使用Node2Vec算法学习到网络中每个节点的向量表示, 用以计算节点间的相似度, 其次, 利用节点影响力函数计算节点影响力并找出种子节点, 然后基于每个种子节点进行社区的扩展优化, 最终挖掘出高质量的重叠社区结构. 本文选取多个真实网络进行了对比实验, 结果表明, 本文所提出的算法能够在保证良好稳定性的前提下发现高质量的社区结构.     

融合链接预测相似度矩阵的属性网络嵌入算法

在属性网络中,与节点相关联的属性信息有助于提升网络嵌入各种任务的性能,但网络是一种图状结构,节点不仅包含属性信息还隐含着丰富的结构信息。为了充分融合结构信息,首先通过定义节点的影响力特性、空间关系特征;然后根据链接预测领域基于相似度的定义构建相似度矩阵,将节点二元组中的关联向量映射到相似度矩阵这一关系空间中,从而保留与节点相关的结构向量信息;再基于图的拉普拉斯矩阵融合属性信息和标签特征,将上述三类信息集成到一个最优化框架中;最后,通过二阶导数求局部最大值计算投影矩阵获取节点的特征表示进行网络嵌入。实验结果表明,提出的算法能够充分利用节点二元组的邻接结构信息,相比于其他基准网络嵌入算法,本模型在节点分类任务上取得了更好的结果。     

FCM框架下的重叠社区发现算法

重叠社区发现是近些年来社交网络分析中的一个热门课题,但大部分算法有着时间复杂度高或健壮性差的缺点。本文构造了一种节点相似度计算方法,针对FCM的缺陷提出改进,从而利用该改进的Fuzzyc-means计算出每个节点的隶属度;然后设定阅值决定每个节点的类别,实现了重叠社区发现;接下来在真实数据集上的对比实验结果表明该算法在有较低的时间复杂度同时能有效的发现网络中的重叠社区结构。     

CAMAS: A cluster-aware multiagent system for attributed graph clustering

Attributed graphs describe nodes via attribute vectors and also relationships between different nodes via edges. To partition nodes into clusters with tighter correlations, an effective way is applying clustering techniques on attributed graphs based on various criteria such as node connectivity and/or attribute similarity. Even though clusters typically form around nodes with tight edges and similar attributes, existing methods have only focused on one of these two data modalities. In this paper, we comprehend each node as an autonomous agent and develop an accurate and scalable multiagent system for extracting overlapping clusters in attributed graphs. First, a kernel function with a tunable bandwidth factor δ is introduced to measure the influence of each agent, and those agents with highest local influence can be viewed as the “leader” agents. Then, a novel local expansion strategy is proposed, which can be applied by each leader agent to absorb the most relevant followers in the graph. Finally, we design the cluster-aware multiagent system (CAMAS), in which agents communicate with each other freely under an efficient communication mechanism. Using the proposed multiagent system, we are able to uncover the optimal overlapping cluster configuration, i.e. nodes within one cluster are not only connected closely with each other but also with similar attributes. Our method is highly efficient, and the computational time is shown that nearly linearly dependent on the number of edges when δ ∈ [0.5, 1). Finally, applications of the proposed method on a variety of synthetic benchmark graphs and real-life attributed graphs are demonstrated to verify the systematic performance.     

A note on leader election in directed split-stars and directed alternating group graphs

A leader node is defined to be any node of the network unambiguously identified by some characteristics. In this paper, we first present a distributed algorithm for finding a leader node of a directed split-star. Moreover, an efficient self-stabilizing leader election algorithm that converges with linear rounds is proposed for directed split-stars. Actually, the distributed algorithm and the self-stabilizing algorithm are also applicable to the problem of directed alternating group graphs. As far as we know, no self-stabilizing leader election algorithm was known for the two graphs.     

Novel distributed robust adaptive consensus protocols for linear multi-agent systems with directed graphs and external disturbances

This paper addresses the distributed consensus protocol design problem for linear multi-agent systems with directed graphs and external unmatched disturbances. Novel distributed adaptive consensus protocols are proposed to achieve leader–follower consensus for any directed graph containing a directed spanning tree with the leader as the root node and leaderless consensus for strongly connected directed graphs. It is pointed out that the adaptive protocols involve undesirable parameter drift phenomenon when bounded external disturbances exist. By using the σ modification technique, distributed robust adaptive consensus protocols are designed to guarantee the ultimate boundedness of both the consensus error and the adaptive coupling weights in the presence of external disturbances. All the adaptive protocols in this paper are fully distributed, relying on only the agent dynamics and the relative states of neighbouring agents.     

Content-based image retrieval based on a fuzzy approach

A typical content-based image retrieval (CBIR) system would need to handle the vagueness in the user queries as well as the inherent uncertainty in image representation, similarity measure, and relevance feedback. We discuss how fuzzy set theory can be effectively used for this purpose and describe an image retrieval system called FIRST (fuzzy image retrieval system) which incorporates many of these ideas. FIRST can handle exemplar-based, graphical-sketch-based, as well as linguistic queries involving region labels, attributes, and spatial relations. FIRST uses fuzzy attributed relational graphs (FARGs) to represent images, where each node in the graph represents an image region and each edge represents a relation between two regions. The given query is converted to a FARG, and a low-complexity fuzzy graph matching algorithm is used to compare the query graph with the FARGs in the database. The use of an indexing scheme based on a leader clustering algorithm avoids an exhaustive search of the FARG database. We quantify the retrieval performance of the system in terms of several standard measures.     

Flocking coordination using active leader and local information

In this paper coordination of a group of agents by a coordinator agent, called the leader‐agent (LA), is discussed. Agents have identical finite sensing radii and access only the local information. Inter‐agent interactions bring them together as a multi‐agent network system. In a recently proposed flocking algorithm using a virtual leader, it is proved that if a fraction of agents are informed then the center of mass of the multi‐agent system tracks the virtual leader. The minimum number of the fraction is always greater than one. In this paper the only informed agent is the LA. Interaction functions are defined to make the model of the multi‐agent system suitable for the design of the coordination control algorithm. The leader‐agent accesses its local data and actively plans its motions through a nonlinear control method. It is proven in this paper that the multi‐agent system tracks the virtual leader and a locally defined point near the LA converges to the position of the virtual leader. It is also shown that in this case, center of mass of the multi‐agent system follows the virtual leader but with a finite distance. We have also discussed positive effects of the LA on network connectivity. Finally illustrative examples are added as well. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Application of if-conversion to verification and optimization of workflows

Based on the workflow analysis graphs proposed in [1] and the well-known if-conversion method [2], a new algorithm of workflow verification is developed. This algorithm is based on the Boolean algebra principles, which is reflected in its name—Boolean Verification Algorithm (BVA). The BVA operates with arbitrary overlapping structures of the graph and with cycles. In the case of dense graphs, the time complexity of the algorithm does not exceed that of most other algorithms of workflow verification [3–6]. In the course of verification, the BVA determines an execution condition for each node of the graph, which makes it possible to create an additional algorithm of workflow optimization. Unlike the well-known algorithms of structural workflow optimization based on pattern transformations [7, 8], the proposed optimization algorithm allows for maximum (within a cycle) parallelization of workflows containing arbitrary overlapping structures.     

Second-order tracking control for leader-follower multi-agent flocking in directed graphs with switching topology

This paper investigates the flocking problem for leader-follower multi-agent systems in directed graphs with switching topology. A decentralized state control rule, namely, a second-order protocol, is designed for each agent to track the leader. And it is proved that the proposed control scheme can effectively estimate the tracking error of each agent when the leader is active. Particularly, to ensure the tracking error can be estimated, the following two questions are solved: (1) How many agents are needed to connect to the leader? (2) How should these connections be distributed? Finally, a simple example is also given to verify the effectiveness of the proposed theorems.     

Tracking control for multi-agent consensus with an active leader and variable topology

In this paper, we consider a multi-agent consensus problem with an active leader and variable interconnection topology. The state of the considered leader not only keeps changing but also may not be measured. To track such a leader, a neighbor-based local controller together with a neighbor-based state-estimation rule is given for each autonomous agent. Then we prove that, with the proposed control scheme, each agent can follow the leader if the (acceleration) input of the active leader is known, and the tracking error is estimated if the input of the leader is unknown.     

LPX: Overlapping community detection based on X‐means and label propagation algorithm in attributed networks

Traditional community detection methods in attributed networks (eg, social network) usually disregard abundant node attribute information and only focus on structural information of a graph. Existing community detection methods in attributed networks are mostly applied in the detection of nonoverlapping communities and cannot be directly used to detect the overlapping structures. This article proposes an overlapping community detection algorithm in attributed networks. First, we employ the modified X‐means algorithm to cluster attributes to form different themes. Second, we employ the label propagation algorithm (LPA), which is based on neighborhood network conductance for priority and the rule of theme weight, to detect communities in each theme. Finally, we perform redundant processing to form the final community division. The proposed algorithm improves the X‐means algorithm to avoid the effects of outliers. Problems of LPA such as instability of division and adjacent communities being easily merged can be corrected by prioritizing the node neighborhood network conductance. As the community is detected in the attribute subspace, the algorithm can find overlapping communities. Experimental results on real‐attributed and synthetic‐attributed networks show that the performance of the proposed algorithm is excellent with multiple evaluation metrics.