Structure of n-clique networks embedded in a complex network

Takemoto, Kazuhiro; Oosawa, Chikoo; Akutsu, Tatsuya

doi:10.1016/j.physa.2007.02.042

Cited by 20 publications

(16 citation statements)

References 25 publications

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“…Secondly, we present an analysis aimed at community structure in Chinese character network. Thirdly, we study the community structure evolution of five different networks and the community structure can help us to understand the global structures [14]. Finally, we draw the conclusions in the last section.…”

Section: Introductionmentioning

confidence: 90%

Complex Networks: Statistical Properties, Community Structure, and Evolution

Zhang

Cao

2015

Mathematical Problems in Engineering

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We investigate the function for different networks based on complex network theory. In this paper, we choose five data sets from various areas to study. In the study of Chinese network, scale-free effect and hierarchical structure features are found in this complex system. These results indicate that the discovered features of Chinese character structure reflect the combination nature of Chinese characters. In addition, we study the community structure in Chinese character network. We can find that community structure is always considered as one of the most significant features in complex networks, and it plays an important role in the topology and function of the networks. Furthermore, we cut all the nodes in the different networks from low degree to high degree and then obtain many networks with different scale. According to the study, two interesting results have been obtained. First, the relationship between the node number of the maximum communities and the number of communities in the corresponding networks is studied and it is linear. Second, when the number of nodes in the maximum communities is increasing, the increasing tendency of the number of its edges slows down; we predict the complex networks have sparsity. The study effectively explains the characteristic and community structure evolution on different networks.

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Section: Introductionmentioning

confidence: 90%

Complex Networks: Statistical Properties, Community Structure, and Evolution

Zhang

Cao

2015

Mathematical Problems in Engineering

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“…Next we present an analysis aimed at statistical properties of n-node cliques existed in near-synonyms network. The n-node cliques are very valuable since they help us to understand the global structures [11]. Finally, we draw the conclusions in last section.…”

Section: Introductionmentioning

confidence: 94%

Statistical Properties of Chinese Near-synonyms Network

Li¹,

Chen²,

Luo³

et al. 2013

Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering

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We investigate Chinese near-synonyms (include synonyms and near-synonyms) based on complex network theory. In the study of near-synonyms network, scale-free effect and hierarchical structure features are found in this complex system. In particular, we find that n-node cliques exist widely in near-synonyms network. N-node clique structure is a powerful tool for understanding global structures of complex networks. Through the analysis of nnode cliques, near-synonyms are clustered in clique style. The structures properties of n-node cliques in a way may explain the associative mechanism and information storage in human brain.

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“…In earlier researches, Chapple et al adopted the number of patent permission as the index to measure technology transfer performance while Thursby used the transaction price of patents as the index to evaluate the transfer efficiency [13] [14].…”

Section: Indexes Of Technology Transfer Performancementioning

confidence: 99%

Empirical Analysis of Network Reciprocity’s Impacts on Universities’ Cross-Region Technology Transfer Performance

Tang¹

2017

JSS

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Based on the analysis of the network theories, assumptions are raised about how the reciprocity influences the transfer, and theoretical models are established accordingly. Variables reflecting network reciprocity and technology transfer performance are chosen and measured based on the study of assumptions. Regression analysis of panel data is adopted to study the relation between the variables, and the result is used to verify the assumptions. Finally, corresponding advices on policy making and future study are raised according to the research results. Through empirical analysis, it is illustrated that the performance of cross-region technology transfer is improved with the increase of the region's reciprocity relationship in the transfer network. The reciprocity relationship can significantly promote the import, export and transfer level of universities' technologies. The region's participation level of reciprocity cliques can accelerate the export of technology but has no significant impact on technology transfer.

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Structure of n-clique networks embedded in a complex network

Cited by 20 publications

References 25 publications

Complex Networks: Statistical Properties, Community Structure, and Evolution

Complex Networks: Statistical Properties, Community Structure, and Evolution

Statistical Properties of Chinese Near-synonyms Network

Empirical Analysis of Network Reciprocity’s Impacts on Universities’ Cross-Region Technology Transfer Performance

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