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reproducing Shimada et al. 2016 "Graph distance for complex networks" paper

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00-generate-A-compute-hamming-dist.ipynb

00-generate-A-compute-hamming-dist.ipynb

 
 

00-generate-simulated-graphs.ipynb

00-generate-simulated-graphs.ipynb

 
 

01-sgd-WS-models-fixed-k-unnormL.ipynb

01-sgd-WS-models-fixed-k-unnormL.ipynb

 
 

02-sgd-WS-models-fixed-p-unnormL.ipynb

02-sgd-WS-models-fixed-p-unnormL.ipynb

 
 

03-sgd-ER-models-fixed-N-unnormL.ipynb

03-sgd-ER-models-fixed-N-unnormL.ipynb

 
 

04-visualize-results.ipynb

04-visualize-results.ipynb

 
 

05-thesis-figures.ipynb

05-thesis-figures.ipynb

 
 

Erdos_Renyi.py

Erdos_Renyi.py

 
 

Graph.py

Graph.py

 
 

README.md

README.md

 
 

SGD.py

SGD.py

 
 

Watts_Strogatz.py

Watts_Strogatz.py

 
 

fun.py

fun.py

 
 

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Graph distance for complex networks

In this repository I try to reproduce the experiments performed on: https://www.nature.com/articles/srep34944 with the purpose of extending the formulation in terms of the normalized Laplacian matrix representation.

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reproducing Shimada et al. 2016 "Graph distance for complex networks" paper

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graph-distance spectral-graph-distance ws-models

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