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Jian Chao Zeng

Bio: Jian Chao Zeng is an academic researcher from Taiyuan University of Science and Technology. The author has contributed to research in topics: Degradation (telecommunications) & Modularity. The author has an hindex of 1, co-authored 5 publications receiving 78 citations.

Papers
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Journal ArticleDOI
TL;DR: A quantitative function for community partition, named communitarity or C value, is proposed and it is demonstrated that the quantitative is superior to modularity Q and modularity density D.
Abstract: Detecting and characterizing the community structure of complex network is fundamental. We compare the classical optimization indexes of modularity and modularity density, which are quality indexes for a partition of a network into communities. Based on this, we propose a quantitative function for community partition, named communitarity or C value. We demonstrate that the quantitative is superior to modularity Q and modularity density D. Both theoretical and numerical results show that optimizing the new index not only can resolve small modules, but also can correctly identify the number of communities.

89 citations

Journal ArticleDOI
TL;DR: In this paper , the effects of stochastic dependence between components on the degradation process and the remaining useful life (RUL) of a system were investigated, and a degradation model integrating the effects between components was formulated, and the probability density function of the RUL was derived for multi-component systems with different structures.

2 citations

Journal ArticleDOI
TL;DR: This paper studied the problem of deadlock performance analysis for collaborative design process model based on extended objective Petri Net by analyzing the collectivity requirement for the machinery products collaborative design system to be built and OPN invariant analysis theory has been adopted.
Abstract: This paper studied the problem of deadlock performance analysis for collaborative design process model based on extended objective Petri Net (OPN). Firstly, the definition of CSCD extended OPN model based on decision rules has been proposed by analyzing the collectivity requirement for the machinery products collaborative design system to be built. Besides, models for the OPN model of process controlling nets and design unit objective class. Then OPN invariant analysis theory has been adopted to analyze the dynamic performance of deadlock to the part design unit by building its object communication net. The dynamic performance of the whole model could be obtained by analyzing the performance of all the design unit object class.

1 citations

Journal ArticleDOI
TL;DR: Simulation results show that under the same experimental the accuracy of the algorithm is better than the DV-HOP algorithm, and can meet the requirements of localization on the coalface environment.
Abstract: For the complex environment and multi influencing factor in coalface, Wireless Sensor Networks (WSN) is a effective solutions for the coal face of environmental monitoring and miner positioning. Based on the environmental features of the coalface, a WSN deployment paradigm is proposed in this paper, and the DV-HOP localization algorithm is determined to be the miner location solution. Due to the low accuracy of DV-HOP algorithm on condition of the uneven structure of the coalface WSN and the big distance gap between adjacent node, a RSSI-based weight DV-HOP algorithm is proposed. In this algorithm the hops of adjacent node is weighted by the RSS of flooding packets transmitted between adjacent nodes, which associated the hops with the distance of adjacent nodes. Simulation results show that under the same experimental the accuracy of our algorithm is better than the DV-HOP algorithm, and can meet the requirements of localization on the coalface environment.

1 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: A thorough exposition of community structure, or clustering, is attempted, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists.
Abstract: The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

9,057 citations

Journal ArticleDOI
TL;DR: Progress towards quantifying medium- and large-scale structures within complex networks is reviewed, with a focus on subsystems defined by only a subset of nodes and edges.
Abstract: Networks have proved to be useful representations of complex systems. Within these networks, there are typically a number of subsystems defined by only a subset of nodes and edges. Detecting these structures often provides important information about the organization and functioning of the overall network. Here, progress towards quantifying medium- and large-scale structures within complex networks is reviewed.

748 citations

Journal ArticleDOI
TL;DR: Based on the proposed discrete framework, a multiobjective discrete particle swarm optimization algorithm is proposed to solve the network clustering problem and the decomposition mechanism is adopted.
Abstract: The field of complex network clustering has been very active in the past several years. In this paper, a discrete framework of the particle swarm optimization algorithm is proposed. Based on the proposed discrete framework, a multiobjective discrete particle swarm optimization algorithm is proposed to solve the network clustering problem. The decomposition mechanism is adopted. A problem-specific population initialization method based on label propagation and a turbulence operator are introduced. In the proposed method, two evaluation objectives termed as kernel k-means and ratio cut are to be minimized. However, the two objectives can only be used to handle unsigned networks. In order to deal with signed networks, they have been extended to the signed version. The clustering performances of the proposed algorithm have been validated on signed networks and unsigned networks. Extensive experimental studies compared with ten state-of-the-art approaches prove that the proposed algorithm is effective and promising.

342 citations

Journal ArticleDOI
TL;DR: A survey of the metrics used for community detection and evaluation can be found in this paper, where the authors also conduct experiments on synthetic and real networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.
Abstract: Detecting and analyzing dense groups or communities from social and information networks has attracted immense attention over the last decade due to its enormous applicability in different domains. Community detection is an ill-defined problem, as the nature of the communities is not known in advance. The problem has turned even more complicated due to the fact that communities emerge in the network in various forms such as disjoint, overlapping, and hierarchical. Various heuristics have been proposed to address these challenges, depending on the application in hand. All these heuristics have been materialized in the form of new metrics, which in most cases are used as optimization functions for detecting the community structure, or provide an indication of the goodness of detected communities during evaluation. Over the last decade, a large number of such metrics have been proposed. Thus, there arises a need for an organized and detailed survey of the metrics proposed for community detection and evaluation. Here, we present a survey of the start-of-the-art metrics used for the detection and the evaluation of community structure. We also conduct experiments on synthetic and real networks to present a comparative analysis of these metrics in measuring the goodness of the underlying community structure.

189 citations