Vertex Mesh Simplification Algorithm Based on Curvature and Distance Metric

2016 ◽  
pp. 152-160
Author(s):  
Yunliang Jiang ◽  
Wuyang Nie ◽  
Liang Tang ◽  
Yong Liu ◽  
Ronghua Liang ◽  
...  
Keyword(s):  
Mesh Simplification ◽  
Distance Metric

Contextual-distance Metric Based Laplacian Maximum Margin Criterion

2011 ◽  
Vol 36 (12) ◽  
pp. 1661-1673
Author(s):  
Jun GAO ◽  
Shi-Tong WANG ◽  
Xiao-Ming WANG
Keyword(s):  
Distance Metric ◽  
Maximum Margin ◽  
Maximum Margin Criterion

Feature Preserving Mesh Simplification Algorithm Based on Square Volume Measure

2009 ◽  
Vol 32 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Yuan-Feng ZHOU ◽  
Cai-Ming ZHANG ◽  
Ping HE
Keyword(s):  
Mesh Simplification ◽  
Volume Measure ◽  
Feature Preserving

scholarly journals Distance metric learning for graph structured data

2021 ◽  
Author(s):  
Tomoki Yoshida ◽  
Ichiro Takeuchi ◽  
Masayuki Karasuyama
Keyword(s):  
Metric Learning ◽  
Structured Data ◽  
Distance Metric Learning ◽  
Distance Metric

scholarly journals Betweenness centrality for temporal multiplexes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Silvia Zaoli ◽  
Piero Mazzarisi ◽  
Fabrizio Lillo
Keyword(s):  
Information Flow ◽  
Real World ◽  
Betweenness Centrality ◽  
Temporal Structure ◽  
Shortest Paths ◽  
Single Layer ◽  
Distance Metric ◽  
Definition Of

AbstractBetweenness centrality quantifies the importance of a vertex for the information flow in a network. The standard betweenness centrality applies to static single-layer networks, but many real world networks are both dynamic and made of several layers. We propose a definition of betweenness centrality for temporal multiplexes. This definition accounts for the topological and temporal structure and for the duration of paths in the determination of the shortest paths. We propose an algorithm to compute the new metric using a mapping to a static graph. We apply the metric to a dataset of $$\sim 20$$ ∼ 20 k European flights and compare the results with those obtained with static or single-layer metrics. The differences in the airports rankings highlight the importance of considering the temporal multiplex structure and an appropriate distance metric.

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