Graph Theory for Dimensionality Reduction: A Case Study to Prognosticate Parkinson's

2020 ◽  
Author(s):  
Shithi Maitra ◽  
Tonmoy Hossain ◽  
Khan Md. Hasib ◽  
Fairuz Shadmani Shishir
Keyword(s):  
Graph Theory ◽  
Dimensionality Reduction

scholarly journals Graph theory for feature extraction and classification: A migraine pathology case study

2014 ◽  
Vol 24 (6) ◽  
pp. 2979-2986
Author(s):  
Fernando Jorge-Hernandez ◽  
Yolanda Garcia Chimeno ◽  
Begonya Garcia-Zapirain ◽  
Alberto Cabrera Zubizarreta ◽  
Maria Angeles Gomez Beldarrain ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Graph Theory

On Non-Eliminative Structuralism. Unlabeled Graphs as a Case Study, Part A†

2020 ◽  
Vol 28 (3) ◽  
pp. 317-346
Author(s):  
Hannes Leitgeb
Keyword(s):  
Graph Theory ◽  
Axiomatic Theory ◽  
Concrete Case ◽  
Graph Theoretic ◽  
Philosophical Interpretation ◽  
Criterion Of Identity

Abstract This is Part A of an article that defends non-eliminative structuralism about mathematics by means of a concrete case study: a theory of unlabeled graphs. Part A summarizes the general attractions of non-eliminative structuralism. Afterwards, it motivates an understanding of unlabeled graphs as structures sui generis and develops a corresponding axiomatic theory of unlabeled graphs. As the theory demonstrates, graph theory can be developed consistently without eliminating unlabeled graphs in favour of sets; and the usual structuralist criterion of identity can be applied successfully in graph-theoretic proofs. Part B will turn to the philosophical interpretation and assessment of the theory.

scholarly journals Analyses of Crime Patterns in NIBRS Data Based on a Novel Graph Theory Clustering Method: Virginia as a Case Study

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Peixin Zhao ◽  
Marjorie Darrah ◽  
Jim Nolan ◽  
Cun-Quan Zhang
Keyword(s):  
Graph Theory ◽  
Reporting System ◽  
Test Case ◽  
Clustering Method ◽  
Crime Patterns ◽  
Crime Type ◽  
Crime Types

This paper suggests a novel clustering method for analyzing the National Incident-Based Reporting System (NIBRS) data, which include the determination of correlation of different crime types, the development of a likelihood index for crimes to occur in a jurisdiction, and the clustering of jurisdictions based on crime type. The method was tested by using the 2005 assault data from 121 jurisdictions in Virginia as a test case. The analyses of these data show that some different crime types are correlated and some different crime parameters are correlated with different crime types. The analyses also show that certain jurisdictions within Virginia share certain crime patterns. This information assists with constructing a pattern for a specific crime type and can be used to determine whether a jurisdiction may be more likely to see this type of crime occur in their area.

Identify the Bottleneck of Water Network by Using Graph Theory

2012 ◽  
Vol 433-440 ◽  
pp. 4794-4797 ◽  
Author(s):  
Ren Jie Shen ◽  
Qing Gang Jia ◽  
Yan Yan Liang ◽  
Jing Zhang
Keyword(s):  
Graph Theory ◽  
Flow Rate ◽  
System Integration ◽  
Water System ◽  
Water Networks ◽  
Water Network ◽  
Bottleneck Identification

Water system integration which aims on the bottleneck of the water network is the only effective method. Therefore, it is essential and significant to study the bottleneck identification of water networks. In this paper, the definition and fundamental characteristics of the bottleneck about flow rate in water network are given. The graph theory was engaged to identify the bottleneck of water network. A case study is used to show the method.

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