基于多节点社团意识系统的属性图聚类算法

属性图用属性向量描述节点,用边描述节点间的关系。为了把节点划分为具有紧密联系的社团,一种有效的方法是对属性图进行聚类。聚类方法有不同的标准,如节点连接度和属性相似度。虽然社团一般是围绕紧密的连边和相似的属性值的节点形成,但是目前的方法都只关注了这两种数据形式中的一种。通过给每个节点赋予一个自治域,提出一个准确且可延展的多节点系统用于提取属性图中的重叠社团。首先,引入带有可调带宽因子的核函数用于测度每个节点的影响力,具有最高局部影响力的节点可以被看作领导节点。其次,提出一种新颖的局部扩展策略,使每一个领导节点能够吸收属性图中相关性最强的跟随者。接着,设计了多节点社团意识系统,该系统为节点之间的充分沟通提供了必要的条件,从而能够得出最优的重叠社团结构。社团中的节点不仅互相联系紧密,而且也有相似的属性。该算法的计算复杂度在特定带宽条件下近似于连边数目的线性函数。最后,基于标准属性图和真实属性图的实验验证了该系统的有效性和高效性。     

Pathfinder: Visual Analysis of Paths in Graphs

The analysis of paths in graphs is highly relevant in many domains. Typically, path‐related tasks are performed in node‐link layouts. Unfortunately, graph layouts often do not scale to the size of many real world networks. Also, many networks are multivariate, i.e., contain rich attribute sets associated with the nodes and edges. These attributes are often critical in judging paths, but directly visualizing attributes in a graph layout exacerbates the scalability problem. In this paper, we present visual analysis solutions dedicated to path‐related tasks in large and highly multivariate graphs. We show that by focusing on paths, we can address the scalability problem of multivariate graph visualization, equipping analysts with a powerful tool to explore large graphs. We introduce Pathfinder, a technique that provides visual methods to query paths, while considering various constraints. The resulting set of paths is visualized in both a ranked list and as a node‐link diagram. For the paths in the list, we display rich attribute data associated with nodes and edges, and the node‐link diagram provides topological context. The paths can be ranked based on topological properties, such as path length or average node degree, and scores derived from attribute data. Pathfinder is designed to scale to graphs with tens of thousands of nodes and edges by employing strategies such as incremental query results. We demonstrate Pathfinder's fitness for use in scenarios with data from a coauthor network and biological pathways.     

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。     

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.     

Finding minimum transmission radii for preserving connectivity and constructing minimal spanning trees in ad hoc and sensor networks

Ad hoc networks are normally modeled by unit graphs, where two nodes are connected if and only if their distance is at most the transmission radius R, equal for all nodes. Larger than necessary values of R cause communication interference and consumption of increased energy, while smaller values may disable data communication tasks such as routing and broadcasting. It was recognized that the minimum value of R that preserves the network connectivity is equal to the longest edge in the minimum spanning tree. However, all existing solutions for finding R rely on algorithms that require global network knowledge or inefficient straightforward distributed adaptations of centralized algorithms. This article proposes to use the longest LMST (local minimum spanning tree, recently proposed message free approximation of MST) edge to approximate R using a wave propagation quasi-localized algorithm. The differences between exact and so approximated values of R are estimated for two and three-dimensional random unit graphs. Despite small number of additional edges in LMST with respect to MST (under 5%), they can extend R by about 33% its range on networks with up to 500 nodes. We then prove that MST is a subset of LMST and describe a quasi-localized scheme for constructing MST from LMST. It needs less than 7 messages per node on average (for networks up to 500 nodes). The algorithm eliminates LMST edges which are not in MST by a loop breakage procedure, which iteratively follows dangling edges from leaves to LMST loops, and breaks loops by eliminating their longest edges, until the procedure finishes at a single node (as a byproduct, this single node can also be considered as an elected leader of the network). This so elected leader also learns longest MST edge in the process, and may broadcast it to other nodes. We also describe an algorithm for updating MST when a single node is added to the network.     

A hybrid heuristic for the overlapping cluster editing problem

Clustering problems are applicable to several areas of science. Approaches and algorithms are as varied as the applications. From a graph theory perspective, clustering can be generated by partitioning an input graph into a vertex-disjoint union of cliques (clusters) through addition and deletion of edges. Finding the minimum number of edges additions and deletions required to cluster data that can be represented as graphs is a well-known problem in combinatorial optimization, often referred to as cluster editing problem. However, many real-world clustering applications are characterized by overlapping clusters, that is, clusters that are non-disjoint. In these situations cluster editing cannot be applied to these problems. Literature concerning a relaxation of the cluster editing, where clusters can overlap, is scarce. In this work, we propose the overlapping cluster editing problem, a variation of the cluster editing where the goal is to partition a graph, also by editing edges, into maximal cliques that are not necessarily disjoint. In addition, we also present three slightly different versions of a hybrid heuristic to solve this problem. Each hybrid heuristic is based on coupling two metaheuristicsthat, together, generate a set of clusters; and one of three mixed-integer linear programming models, also introduced in this paper, that uses these clusters as input. The objective with the metaheuristics is to limit the solution exploration space in the models’ resolution, therefore reducing its computational time.Tests results show that the all proposed hybrid heuristic versions are able to generate good-quality overlapping cluster editing solutions. In particular, one version of the hybrid heuristic achieved, at a low computational cost, the best results in 51 of 112 randomly-generated graphs. Although the other two hybrid heuristic versions have harder to solve models, they obtained reasonable results in medium-sized randomly-generated graphs. In addition, the hybrid heuristic achieved good results identifying labeled overlapping clusters in a supervised data set experiment. Furthermore, we also show that, with our new problem definition, clustering a vertex in more than one cluster can reduce the edges editing cost.     

Attributed graph transformation with node type inheritance

The aim of this paper is to integrate typed attributed graph transformation with node type inheritance. Borrowing concepts from object oriented systems, the main idea is to enrich the attributed type graph with an inheritance relation and a set of abstract nodes. In this way, a node type inherits the attributes and edges of all its ancestors. Based on these concepts, it is possible to define abstract productions, containing abstract nodes. These productions are equivalent to a number of concrete productions, resulting from the substitution of the abstract node types by the node types in their inheritance clan. Therefore, productions become more compact and suitable for their use in combination with meta-modelling. The main results of this paper show that attributed graph transformation with node type inheritance is fully compatible with the existing concept of typed attributed graph transformation.     

Community Discovery Algorithm Based on Multi-Relationship Embedding

Complex systems in the real world often can be modeled as network structures, and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks. Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes. However, many real-world networks consist of multiple types of nodes and edges, and there may be rich semantic information on nodes and edges. The methods for single-layer networks cannot effectively tackle multi-layer information, multi-relationship information, and attribute information. This paper proposes a community discovery algorithm based on multi-relationship embedding. The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder. The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network (GCN) to obtain the final node embedding matrix. This strategy allows capturing of rich structural and attributes information in multi-relational networks. Experiments were conducted on different datasets with baselines, and the results show that the proposed algorithm obtains significant performance improvement in community discovery, node clustering, and similarity search tasks, and compared to the baseline with the best performance, the proposed algorithm achieves an average improvement of 3.1% on Macro-F1 and 4.7% on Micro-F1, which proves the effectiveness of the proposed algorithm.     

AHNG: Representation learning on attributed heterogeneous network

Network embedding aims to encode nodes into a low-dimensional space with the structure and inherent properties of the networks preserved. It is an upstream technique for network analyses such as link prediction and node clustering. Most existing efforts are devoted to homogeneous or heterogeneous plain networks. However, networks in real-world scenarios are usually heterogeneous and not plain, i.e., they contain multi-type nodes/links and diverse node attributes. We refer such kind of networks with both heterogeneities and attributes as attributed heterogeneous networks (AHNs). Embedding AHNs faces two challenges: (1) how to fuse heterogeneous information sources including network structures, semantic information and node attributes; (2) how to capture uncertainty of node embeddings caused by diverse attributes. To tackle these challenges, we propose a unified embedding model which represents each node in an AHN with a Gaussian distribution (AHNG). AHNG fuses multi-type nodes/links and diverse attributes through a two-layer neural network and captures the uncertainty by embedding nodes as Gaussian distributions. Furthermore, the incorporation of node attributes makes AHNG inductive, embedding previously unseen nodes or isolated nodes without additional training. Extensive experiments on a large real-world dataset validate the effectiveness and efficiency of the proposed model.     

A block-based generative model for attributed network embedding

Attributed network embedding has attracted plenty of interest in recent years. It aims to learn task-independent, low-dimensional, and continuous vectors for nodes preserving both topology and attribute information. Most of the existing methods, such as random-walk based methods and GCNs, mainly focus on the local information, i.e., the attributes of the neighbours. Thus, they have been well studied for assortative networks (i.e., networks with communities) but ignored disassortative networks (i.e., networks with multipartite, hubs, and hybrid structures), which are common in the real world. To model both assortative and disassortative networks, we propose a block-based generative model for attributed network embedding from a probability perspective. Specifically, the nodes are assigned to several blocks wherein the nodes in the same block share the similar linkage patterns. These patterns can define assortative networks containing communities or disassortative networks with the multipartite, hub, or any hybrid structures. To preserve the attribute information, we assume that each node has a hidden embedding related to its assigned block. We use a neural network to characterize the nonlinearity between node embeddings and node attributes. We perform extensive experiments on real-world and synthetic attributed networks. The results show that our proposed method consistently outperforms state-of-the-art embedding methods for both clustering and classification tasks, especially on disassortative networks.

     

Non-communicative multi-robot coordination in dynamic environments

Within a group of cooperating agents the decision making of an individual agent depends on the actions of the other agents. In dynamic environments, these dependencies will change rapidly as a result of the continuously changing state. Via a context-specific decomposition of the problem into smaller subproblems, coordination graphs offer scalable solutions to the problem of multiagent decision making. In this work, we apply coordination graphs to a continuous (robotic) domain by assigning roles to the agents and then coordinating the different roles. Moreover, we demonstrate that, with some additional assumptions, an agent can predict the actions of the other agents, rendering communication superfluous. We have successfully implemented the proposed method into our UvA Trilearn simulated robot soccer team which won the RoboCup-2003 World Championship in Padova, Italy.     

An adaptive control architecture for leader–follower multiagent systems with stochastic disturbances and sensor and actuator attacks

In this paper, we develop a novel distributed adaptive control architecture for addressing networked multiagent systems subject to stochastic exogenous disturbances with compromised sensor and actuators. Specifically, for a class of linear leader–follower multiagent systems, we develop a new structure of the neighbourhood synchronisation error for the control design protocol of each follower. The proposed control algorithm addresses time-varying multiplicative sensor attacks on the leader state measurements. In addition, the framework addresses time-varying multiplicative actuator attacks on the followers that do not have a communication link with the leader and additive actuator attacks on all follower agents in the network. The proposed adaptive controller guarantees uniform ultimate boundedness of the state tracking error for each agent in a mean-square sense.     

Improvement of the k-means clustering filtering algorithm

In this paper, we present a modified filtering algorithm (MFA) by making use of center variations to speed up clustering process. Our method first divides clusters into static and active groups. We use the information of cluster displacements to reject unlikely cluster centers for all nodes in the kd-tree. We reduce the computational complexity of filtering algorithm (FA) through finding candidates for each node mainly from the set of active cluster centers. Two conditions for determining the set of candidate cluster centers for each node from active clusters are developed. Our approach is different from the major available algorithm, which passes no information from one stage of iteration to the next. Theoretical analysis shows that our method can reduce the computational complexity, in terms of the number of distance calculations, of FA at each stage of iteration by a factor of FC/AC, where FC and AC are the numbers of total clusters and active clusters, respectively. Compared with the FA, our algorithm can effectively reduce the computing time and number of distance calculations. It is noted that our proposed algorithm can generate the same clusters as that produced by hard k-means clustering. The superiority of our method is more remarkable when a larger data set with higher dimension is used.     

Simple multi-visit attribute grammars

An attribute grammar is simple multi-visit if each attribute of a nonterminal has a fixed visit-number associated with it such that, during attribute evaluation, the attributes of a node which have visit-number j are computed at the jth visit to the node. An attribute grammar is l-ordered if for each nonterminal a linear order of its attributes exists such that the attributes of a node can always be evaluated in that order (cf. the work of Kastens).An attribute grammar is simple multi-visit if and only if it is l-ordered. Every noncircular attribute grammar can be transformed into an equivalent simple multi-visit attribute grammar which uses the same semantic operations.For a given distribution of visit-numbers over the attributes, it can be decided in polynomial time whether the attributes can be evaluated according to these visit-numbers. The problem whether an attribute grammar is simple multi-visit is NP-complete.     

CS-Raft:适用于联盟链的拜占庭容错共识算法

针对目前联盟链共识算法的性能不足,提出了一种基于信用评分的可拜占庭容错联盟链共识算法CS-Raft。首先,为所有节点赋予信用评分属性,节点的信用评分根据节点的共识行为、活跃度、加入集群时间等指标进行更新,信用评分越高代表节点可信度越高;其次,根据节点信用评分选取监督节点,监督节点具有检验权,可以参与领导人选举,监督节点的设置可以有效抵抗拜占庭恶意节点的攻击;最后,改善了领导人选举中选票分裂问题,对领导人选举的速度进行提升。经实验分析,CS-Raft算法相较于PBFT算法在实现拜占庭容错的同时,有效地减少了共识时间延迟、提高了系统吞吐量,并加快了其领导人选举速度。     

Cooperative adaptive control for synchronization of second‐order systems with unknown nonlinearities

This paper studies synchronization to a desired trajectory for multi‐agent systems with second‐order integrator dynamics and unknown nonlinearities and disturbances. The agents can have different dynamics and the treatment is for directed graphs with fixed communication topologies. The command generator or leader node dynamics is also nonlinear and unknown. Cooperative tracking adaptive controllers are designed based on each node maintaining a neural network parametric approximator and suitably tuning it to guarantee stability and performance. A Lyapunov‐based proof shows the ultimate boundedness of the tracking error. A simulation example with nodes having second‐order Lagrangian dynamics verifies the performance of the cooperative tracking adaptive controller. Copyright © 2010 John Wiley & Sons, Ltd.     

Attributed relational graph matching based on the nested assignment structure

In this paper, we propose a new ARG matching scheme based on the nested assignment structure to assess the similarity between two attributed relational graphs (ARGs). ARGs are represented by nodes and edges containing unary attributes and binary relations between nodes, respectively. The nested assignment structure consists of inner and outer steps. In the inner step, to form a distance matrix, combinatorial differences between every pair of nodes in two ARGs are computed by using an assignment algorithm. Then, in the outer step, a correspondence between nodes in the two ARGs is established by using an assignment algorithm based on the distance matrix. The proposed ARG matching scheme consists of three procedures as follows: first, in the initializing procedure, the nested assignment structure is performed to generate an initial correspondence between nodes in two ARGs. Next, the correspondence is refined by iteratively performing the updating procedure, which also utilizes the nested assignment structure, until the correspondence does not change. Finally, the verifying procedure can be performed in case that some nodes to be matched in the two ARGs are missing. From experimental results, the proposed ARG matching scheme shows superior matching performance and localizes target objects robustly and correctly even in severely noisy and occluded scenes.     

Visual Inspection of Multivariate Graphs

Most graph visualization techniques focus on the structure of graphs and do not offer support for dealing with node attributes and edge labels. To enable users to detect relations and patterns in terms of data associated with nodes and edges, we present a technique where this data plays a more central role. Nodes and edges are clustered based on associated data. Via direct manipulation users can interactively inspect and query the graph. Questions that can be answered include, “which edge types are activated by specific node attributes?” and, “how and from where can I reach specific types of nodes?” To validate our approach we contrast it with current practice. We also provide several examples where our method was used to study transition graphs that model real‐world systems.     

结合拓扑势与信任度调整的重叠社区发现算法

现实世界中的复杂系统可建模为复杂网络,探究复杂网络中的社区发现算法对于分析复杂网络的拓扑结构和层次结构具有重要作用。早期研究通常将网络中的节点局限在一个社区中,但随着研究的深入发现社区结构呈现重叠特性。针对现有重叠社区发现算法存在划分社区结构不稳定、忽略节点交互和属性等问题,提出一种基于网络拓扑势与信任度调整的重叠社区发现算法。融合节点的属性和结构特征计算节点的拓扑势,依据节点的拓扑势选取核心节点。从核心节点出发构建初始社区群,计算各个社区间的调整信任度,实现社区的合并与再调整,从而识别重叠社区。在多个人工模拟网络和真实网络数据集上的实验结果表明,与基于贪婪派系扩张、种子扩张等的重叠社区发现算法相比,该算法将扩展模块度最高提升至0.719,能有效识别社区结构及重叠节点,提升重叠社区检测性能。     

融合双层注意力机制的属性网络节点嵌入

属性网络嵌入旨在学习网络中节点的低维表示,具有拓扑和属性相似的节点在嵌入空间彼此接近.注意力机制能有效学习网络中节点与其邻居的相对重要性并基于邻居重要性聚合节点表示.据此,提出一种在属性网络中融合双层注意力机制的节点嵌入算法NETA,可以有效地实现属性网络嵌入.该算法首先从拓扑结构捕获直接邻居,基于属性关系捕获间接邻居...