An Early Assessment of Relationship between Spatial Diffusion of Covid-19 and Evolving Worldwide Commercial Aviation Network: Exploring Global and Local Changes in Aviation Networks Using Node Degree Centrality

2020 ◽  
pp. 138-166
Author(s):  
Yongha Park ◽  
Jeongwoong Sohn
Keyword(s):  
Node Degree ◽  
Spatial Diffusion ◽  
Degree Centrality ◽  
Early Assessment ◽  
Commercial Aviation ◽  
Aviation Network ◽  
Global And Local

Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs

2019 ◽  
pp. 1183-1198
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Random Network ◽  
Maximal Clique ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Scale Free ◽  
Centrality Metrics ◽  
Free Network

The authors present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. They consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC), and betweenness centrality (BWC). They define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. The authors observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. They observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).

Clique Size and Centrality Metrics for Analysis of Real-World Network Graphs

2018 ◽  
pp. 6507-6521
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Random Network ◽  
Maximal Clique ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Scale Free ◽  
Centrality Metrics ◽  
Free Network

We present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. We consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC) and betweenness centrality (BWC). We define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. We observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. We observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).

scholarly journals A Binary Search Algorithm for Correlation Study of Decay Centrality vs. Degree Centrality and Closeness Centrality

2017 ◽  
Vol 10 (2) ◽  
pp. 52
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Real World ◽  
Search Algorithm ◽  
Threshold Value ◽  
Search Space ◽  
Correlation Study ◽  
Binary Search ◽  
Closeness Centrality ◽  
Node Degree ◽  
Degree Centrality ◽  
Binary Search Algorithm

Results of correlation study (using Pearson's correlation coefficient, PCC) between decay centrality (DEC) vs. degree centrality (DEG) and closeness centrality (CLC) for a suite of 48 real-world networks indicate an interesting trend: PCC(DEC, DEG) decreases with increase in the decay parameter δ (0 < δ < 1) and PCC(DEC, CLC) decreases with decrease in δ. We make use of this trend of monotonic decrease in the PCC values (from both sides of the δ-search space) and propose a binary search algorithm that (given a threshold value r for the PCC) could be used to identify a value of δ (if one exists, we say there exists a positive δ-spacer) for a real-world network such that PCC(DEC, DEG) ≥ r as well as PCC(DEC, CLC) ≥ r. We show the use of the binary search algorithm to find the maximum Threshold PCC value rmax (such that δ-spacermax is positive) for a real-world network. We observe a very strong correlation between rmax and PCC(DEG, CLC) as well as observe real-world networks with a larger variation in node degree to more likely have a lower rmax value and vice-versa.

scholarly journals DEVELOPING GRAVITY MODEL FOR AIRLINE REGIONAL ROUTE MODELLING

Aviation ◽  
2016 ◽  
Vol 20 (1) ◽  
pp. 32-37 ◽  
Author(s):  
Allan NÕMMIK ◽  
Sven KUKEMELK
Keyword(s):  
Gravity Model ◽  
Route Planning ◽  
Point Of View ◽  
Alternative Methods ◽  
Gravity Potential ◽  
Commercial Aviation ◽  
Travelling Time ◽  
Airline Network ◽  
Aviation Network ◽  
Transportation Routes

The gravity model is a method that is used by transportation researchers, airline network planners and analysts to explain how traffic is distributed between city pairs in correlation to the distance or travelling time between them, which as a result indicates the behaviour of travellers or the performance of the transport connection. How ever, the applicability of the model depends on the reliability of the results, which poses a major issue for researches. The major difficulty is to obtain comparable qualitative insights into the key parameters that are selected. This paper presents a possibility study for the use of the gravity model for regional route planning from the scientific point of view and suggests possibilities of gravity model calibration for airline network analysis including alternative methods for estimating the gravity potential of destinations and measurement of the influence of distance on the potential. The focus of the research is the ability to explain and forecast the development of regional air transportation routes in the commercial aviation network when there is a lack of recorded booking demand data.

scholarly journals Identifying Core Parts in Complex Mechanical Product for Change Management and Sustainable Design

2018 ◽  
Vol 10 (12) ◽  
pp. 4480 ◽  
Author(s):  
Na Zhang ◽  
Yu Yang ◽  
Jianxin Wang ◽  
Baodong Li ◽  
Jiafu Su
Keyword(s):  
Design Process ◽  
Sustainable Design ◽  
Closeness Centrality ◽  
Node Degree ◽  
Global Information ◽  
Degree Centrality ◽  
Centrality Measure ◽  
The Core ◽  
Mechanical Products ◽  
Negative Impacts

Changes in customer needs are unavoidable during the design process of complex mechanical products, and may bring severely negative impacts on product design, such as extra costs and delays. One of the effective ways to prevent and reduce these negative impacts is to evaluate and manage the core parts of the product. Therefore, in this paper, a modified Dempster-Shafer (D-S) evidential approach is proposed for identifying the core parts. Firstly, an undirected weighted network model is constructed to systematically describe the product structure. Secondly, a modified D-S evidential approach is proposed to systematically and scientifically evaluate the core parts, which takes into account the degree of the nodes, the weights of the nodes, the positions of the nodes, and the global information of the network. Finally, the evaluation of the core parts of a wind turbine is carried out to illustrate the effectiveness of the proposed method in the paper. The results show that the modified D-S evidential approach achieves better performance regarding the evaluation of core parts than the node degree centrality measure, node betweenness centrality measure, and node closeness centrality measure.

scholarly journals The miracle (and mirage) of Mexican flight: Aviation development in Mexico, during and after the Second World War

2019 ◽  
Vol 40 (1) ◽  
pp. 25-43
Author(s):  
Peter Soland
Keyword(s):  
Second World War ◽  
World War ◽  
Crucial Point ◽  
Cultural Stereotypes ◽  
Commercial Aviation ◽  
Financial Decisions ◽  
The Usa ◽  
Post War ◽  
Aviation Network ◽  
Second World

This paper explores the development of Mexican commercial aviation (and more specifically the trajectory of Compañía Mexicana de Aviación) against the background of Mexico’s Second World War alliance with the USA and its post-war economic expansion. USA foreign aid allowed Mexican president Manuel Ávila Camacho (1940–46) to further develop the country’s aviation network and personnel. The Second World War’s disruption of tourism allowed Mexico to reap the benefits of a rapidly growing vacation industry. The election of Miguel Aléman in 1946 reinforced commercial aviation and tourism as crucial, co-dependent elements in modernising the country and making Compañía Mexicana de Aviación a symbol of national progress. Although the Second World War emerges as a crucial point in the development of Mexican aviation, the same processes that buoyed commercial airlines also reinforced cultural stereotypes that were exploited for USA tourists and masked reckless financial decisions that nearly bankrupted Compañía Mexicana de Aviación’s in late 1950s.

scholarly journals Community Detection Method Based on Node Density, Degree Centrality, and K-Means Clustering in Complex Network

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1145 ◽  
Author(s):  
Cai ◽  
Zeng ◽  
Wang ◽  
Li ◽  
Hu
Keyword(s):  
Community Detection ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Node Degree ◽  
Degree Centrality ◽  
Large Network ◽  
Node Density ◽  
Initial Node ◽  
Initial Cluster ◽  
Node Similarity

Community detection in networks plays a key role in understanding their structures, and the application of clustering algorithms in community detection tasks in complex networks has attracted intensive attention in recent years. In this paper, based on the definition of uncertainty of node community belongings, the node density is proposed first. After that, the DD (the combination of node density and node degree centrality) is proposed for initial node selection in community detection. Finally, based on the DD and k-means clustering algorithm, we proposed a community detection approach, the density-degree centrality-jaccard-k-means method (DDJKM). The DDJKM algorithm can avoid the problem of random selection of initial cluster centers in conventional k-means clustering algorithms, so that isolated nodes will not be selected as initial cluster centers. Additionally, DDJKM can reduce the iteration times in the clustering process and the over-short distances between the initial cluster centers can be avoided by calculating the node similarity. The proposed method is compared with state-of-the-art algorithms on synthetic networks and real-world networks. The experimental results show the effectiveness of the proposed method in accurately describing the community. The results also show that the DDJKM is practical a approach for the detection of communities with large network datasets.

Research on the Evolution of the Complex Network Theory Based on the International Coal Trade

2014 ◽  
Vol 672-674 ◽  
pp. 2173-2177
Author(s):  
Yang Yang He ◽  
Ling Wang
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Network Theory ◽  
Clustering Coefficient ◽  
Node Degree ◽  
Degree Centrality ◽  
Trade Data ◽  
Complex Network Theory ◽  
The Difference ◽  
Coal Trade

According to the international coal trade data of the years from 1996 to 2011 published by UN COMTRADE (UNSD), it can be inferred that the data is mainly about international trade of raw coal and related coal products. By adopting the theory of complex network analysis, this paper calculates the complex network of international coal trade in the aspect of its density, node degree, centrality, point strength, clustering coefficient. Based on these properties, this paper further analyzes the evolution rule for international coal trade network of raw coal, coal briquettes and ovate coal over the last 16 years, as well as the difference between the pre-and after financial crisis.

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