Research on overlapping structures and evolution properties of co-citation network

Purpose: This paper intends to explore methodologies and indicators for the analysis of overlapping structures and evolution properties in a co-citation network, and provide reference for overlapping structure analysis of other scientific networks.Design/methodology/approach: The Q-value variance is defined to achieve overlapping structures of different levels in the scientific networks. At the same time, analyses for time correlation variance and subject correlation variance are used to present the formation of overlapping structures in scientific networks. As a test, a co-citation network of highly cited papers on Molecular Biology & Genetics from Essential Science Indicator(ESI) is taken as an example for an empirical analysis.Findings: Our research showed that the Q-value variance is effective for achieving the desired overlapping structures. Meanwhile, the time correlation variance and subject correlation variance are equally useful for uncovering the evolution progress of scientific research, and the properties of overlapping structures in the research of co-citation network as well.Research limitations: In this paper, the theoretical analysis and verification of time and subject correlation variances are still at its initial stage. Further studies in this regard need to take actual evolution of research areas into consideration.Practical implications: Evolution properties of overlapping structures pave the way for overlapping and evolution analysis of disciplines or areas, this study is of practical value for the planning of scientific and technical innovation.Originality/value: This paper proposes an analytical method of time correlation variance and subject correlation variance based on the evolution properties of overlapping structures, which would provide the foundation for the evolution analysis of disciplines and interdisciplinary research.

Research on Community Structure in Bus Transport Networks

We abstract the bus transport networks(BTNs)to two kinds of complex networks with space L and spaceP methods respectively.Using improved community detecting algorithm(PKM agglomerative algorithm),we analyzethe community property of two kinds of BTNs graphs.The results show that the BTNs graph described with space Lmethod have obvious community property,but the other kind of BTNs graph described with space P method have not.The reason is that the BTNs graph described with space P method have the intense overlapping community propertyand general community division algorithms can not identify this kind of community structure.To overcome this problem,we propose a novel community structure called N-depth community and present a corresponding community detectingalgorithm,which can detect overlapping community.Applying the novel community structure and detecting algorithmto a BTN evolution model described with space P,whose network property agrees well with real BTNs',we get obviouscommunity property.

Effect of Cambering on the Aerodynamic Performance of Heaving Airfoils

In the present work,a parametric numerical study is conducted in order to assess the effect of airfoil cambering on the aerodynamic performance of rigid heaving airfoils.The incompressible Navier-Stokes equations are solved in their velocity-pressure formulation using a second-order accurate in space and time finite-difference scheme.To tackle the problem of moving boundaries,the governing equations are solved on overlapping structured grids.The numerical simulations are performed at a Reynolds number of Re=1100 and at different values of Strouhal number and reduced frequency.The results obtained show that the airfoil cambering geometric parameter has a strong influence on the average lift coefficient,while it has a smaller impact on the average thrust coefficient and propulsive efficiency of heaving airfoils.

Overlapped groupwise dimension reduction

Existing groupwise dimension reduction requires given group structure to be non-overlapped. This confines its application scope. We aim at groupwise dimension reduction with overlapped group structure or even unknown group structure. To this end, existing groupwise dimension reduction concept is extended to be compatible with overlapped group structure. Then, the envelope method is ameliorated to deal with overlapped groupwise dimension reduction. As an application, Gaussian graphic model is employed to estimate the structure between predictors when the group structure is not given, and the amended envelope method is used for groupwise dimension reduction with graphic structure. Furthermore, the rationale of the proposed estimation procedure is explained at the population level and the estimation consistency is proved at the sample level. Finally, the finite sample performance of the proposed methods is examined via numerical simulations and a body fat data analysis.