Weighted node degree centrality for hypergraphs

2013 ◽  
Author(s):  
Komal Kapoor ◽  
Dhruv Sharma ◽  
Jaideep Srivastava
Keyword(s):  
Node Degree ◽  
Degree Centrality

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 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 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.

scholarly journals NAToRA, a relatedness-pruning method to minimize the loss of dataset size in genetic and omics analyses

2021 ◽  
Author(s):  
Thiago Peixoto Leal ◽  
Vinicius C Furlan ◽  
Mateus Henrique Gouveia ◽  
Julia Maria Saraiva Duarte ◽  
Pablo AS Fonseca ◽  
...  
Keyword(s):  
Large Datasets ◽  
Node Degree ◽  
Degree Centrality ◽  
Web Tool ◽  
Pruning Method ◽  
Dataset Size ◽  
Independent Observations ◽  
Related Individuals ◽  
Minimal Reduction ◽  
Number Of Individuals

Genetic and omics analyses frequently require independent observations, which is not guaranteed in real datasets. When relatedness can not be accounted for, solutions involve removing related individuals (or observations) and, consequently, a reduction of available data. We developed a network-based relatedness-pruning method that minimizes dataset reduction while removing unwanted relationships in a dataset. It uses node degree centrality metric to identify highly connected nodes (or individuals) and implements heuristics that approximate the minimal reduction of a dataset to allow its application to large datasets. NAToRA outperformed two popular methodologies (implemented in software PLINK and KING) by showing the best combination of effective relatedness-pruning, removing all relatives while keeping the largest possible number of individuals in all datasets tested and also, with similar or lesser reduction in genetic diversity. NAToRA is freely available, both as a standalone tool that can be easily incorporated as part of a pipeline, and as a graphical web tool that allows visualization of the relatedness networks. NAToRA also accepts a variety of relationship metrics as input, which facilitates its use. We also present a genealogies simulator software used for different tests performed in the manuscript.

scholarly journals Predicting Node Degree Centrality with the Node Prominence Profile

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Yang Yang ◽  
Yuxiao Dong ◽  
Nitesh V. Chawla
Keyword(s):  
Node Degree ◽  
Degree Centrality

scholarly journals An investigation on the evolution of diabetes data in social Q&A logs

2018 ◽  
Vol 2 (1) ◽  
pp. 37-48
Author(s):  
Yiming Zhao ◽  
Baitong Chen ◽  
Jin Zhang ◽  
Ying Ding ◽  
Jin Mao ◽  
...  
Keyword(s):  
The Other ◽  
Node Degree ◽  
Data Sets ◽  
Degree Centrality ◽  
Vocabulary Size ◽  
Test Analysis ◽  
Log Data ◽  
Topic Evolution ◽  
Graph Density ◽  
Key Terms

AbstractThis study investigates the evolution of diabetics’ concerns based on the analysis of terms in the Diabetes category logs on the Yahoo! Answers website. Two sets of question-and-answer (Q&A) log data were collected: one from December 2, 2005 to December 1, 2006; the other from April 1, 2013 to March 31, 2014. Network analysis and at-test were performed to analyze the differences in diabetics’ concerns between these two data sets. Community detection and topic evolution were used to reveal detailed changes in diabetics’ concerns in the examined period. Increases in average node degree and graph density imply that the vocabulary size that diabetics use to post questions decreases while the scope of questions has become more focused. The networks of key terms in the Q&A log data of 2005–2006 and 2013–2014 are significantly different according to thet-test analysis of the degree centrality and betweenness centrality. Specifically, there is a shift in diabetics’ focus in that they have become more concerned about daily life and other nonmedical issues, including diet, food, and nutrients. The recent changes and the evolution paths of diabetics’ concerns were visualized using an alluvial diagram. The food- and diet-related terms have become prominent, as deduced from the visualization results.

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