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About 10,000 papers for “Complex network”

bus complex network

Cui, Yumeng · Figshare · 2022

This is bus data for research.

Cited by 0Open access

Software Complex Network.

Bing Zhang (129), Guoyan Huang (3826270), Yuqian Wang (619491) et al. · Figshare · 2017

Cited by 0Open access

Sustainability Complex Network

Stefano Fantoni · 2024

Abstract We introduce the concept of quantitative evaluation of a complex network made up of researchers operating in different disciplines and different sectors belonging to life and hard sciences or social science and humanities or industrial and entrepreneurial activities, which, in addition to their disciplinary research, interact within each other in interdisciplinary scientific collaborations on sustainability projects. The complex network that we consider in this paper is of the small-world type, which has been already used in the study of several other biological, technological and social complex systems. This kind of network has a flexible structure which is in between those of the completely regular and the completely random networks. Similarly, the increase of interdisciplinary collaborations amongst scholars having a large and recognized experience in a given disciplinary sector may be favoured by random links arising in facing up specific issues of sustainability. Numerical results are given for a few unweighted networks having up to ten research groups with up to hundred researchers each.

Cited by 1Open access

Complex network analysis.

Tomer Fekete (206954), Neta Zach (178587), Lilianne R. Mujica-Parodi (206964) et al. · Figshare · 2015

<p>ALS results in global changes in the topology of inter-area functional connectivity. Inter area functional connectivity in the 0.03–0.06 Hz band exhibited increased assortativity i.e. correlation in degree between connected nodes in ALS patients. This reflects the existence of hyper-connected sub-cortical motor networks. We present the differences in the neighborhood of the chosen threshold (o.35). * denotes <i>p</i><0.05 corrected using an FDR approach.</p>

Cited by 0Open access

Complex Network Metrology

Electronic mail address: guillaume@liafa.jussieu.fr, Jean‐Loup Guillaume, Matthieu Latapy et al. · Complex Systems · 2005

In order to study complex networks like the Internet, the World Wide Web, social networks, or biological networks, one first has to explore them. This gives a partial and biased view of the real object, which is generally assumed to be representative of the whole. However, until now nobody knows how and how much the measure influences the results. Using the example of the Internet and a rough model of its exploration process, we show that the way a given complex network is explored may strongly influence the observed properties. This leads us to argue for the necessity of developing a science of metrology of complex networks. Its aim would be to study how the partial and biased view of a network relates to the properties of the whole network.

Cited by 13Open access

complex-network-reconstruction

Landry, Nicholas, Thompson, Will, Young, Jean-Gabriel · Zenodo (CERN European Organization for Nuclear Research) · 2024

Response to review

Cited by 0Open access

Complex Network Analysis

Jian James, Daniel Zeng, Richard A. Huff · Journal of international technology and information management · 2013

The clustering coefficient has two definitions that dominate its use, one by Watts and Strogatz, and the second by Newman. It is critical to identify and report the analytic similarities and differences between the two methods in order to give future researchers guidance on the appropriate method to use in their research. This paper reports on an analytical comparison between the two clustering coefficient definitions. We performed the comparison using analytical derivations to show the mathematical relations between the two definitions, the limits on the analysis, and the impact clustering coefficient values derived from the two definitions.

Cited by 0Open access

Complex network Files.

Runfeng Sun (18273646), Wenjia Liu (118146) · Figshare · 2025

<div> This paper explores the urban spatial structure and social stratification of Northern Wei Dynasty Luoyang City, based on <i>Record of the Monasteries of Luoyang</i>. Adopting a spatial humanities approach, the study integrates quantitative approaches, including the application of geographic information systems (GIS) for spatial analysis and complex network analysis. The findings indicate that the spatial organization of Northern Wei Luoyang was primarily concentrated within and around the inner city. People of different social classes and statuses occupied distinct spaces, forming socially stratified groups. As one moves outward from the city’s center, the social rank of these groups decreases. The city’s layout is characterized by a single central axis, with multiple spatial divisions, and the west side being associated with higher-status groups. This spatial arrangement was shaped by factors such as geography, urban development, Central Plains rituals, traditional customs, and ethnic diversity. This study enhances our understanding of the spatial layout characteristics of Northern Wei Luoyang. It provides innovative insights and methods for understanding the city’s structure and its implications for other underground historical cities. Furthermore, it provides valuable data and visualizations that could support future preservation and planning efforts for the city. </div>

Cited by 0Open access

bus complex network

Yumeng Cui · Figshare · 2022

This is bus data for research.

Cited by 0Open access

Network science Complex network Community detection

Pasquale De Meo A, Emilio Ferrara B, Giacomo Fiumara A et al. · 2016

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:

Cited by 0

Complex network perspective on network dynamics

Lang Qin, Ling Zhou, Jar‐Der Luo · 2012

From the complex network perspective, using social network analysis methods, we aim to systematically conduct in-deep research on the emergence and evolution of the investment network in the venture capital industry in China to show the network dynamics within a 12-year period. We develop and test 4 alternative logics of attachment - accumulative advantage, follow-the-trend, multiconnectivity and trust-transfer -- to account for both the structure and dynamics of investment in the venture capital industry. In addition, we map the network dynamics of the field over the years of 1995--2006 and present the results using network visualization. Through systematical analysis of the long term data in the VC field, we can elaborate the action of VCs from the network perspective, supplement the existing literature on investment motivation of VCs, enrich the social meaning of investment behavior and help to better understand the practical condition in this field.

Cited by 0