Graph Visual Rhythm for Vehicular Networks

This framework generates Graph Visual Rhythm (GVR) for vehicular networks, considering temporal graphs.

Dependencies:

Before the execution, you need to check these following dependencies: SUMO 1.2, python, python3, networkx, and matplotlib 2.1.0, seaborn.

Dataset:

TAPASCologne is used, but you can use other traces. You can download it in this link http://kolntrace.project.citi-lab.fr/

Colecting data:

python runner.py <cologne6to8>.sumocfg <tripinfo>.xml

Complex network measures:

python graph.py

List of measures available:

• Degree centrality
• Closeness centrality
• Betweenness centrality
• Local efficiency
• Harmonic centrality
• PageRank

Interface:

python3 interface.py

Output:

Citation:

@misc{git-gvr,
  title = {Graph Visual Rhythm for Vehicular Networks},
  author = {Trindade, Silvana},
  howpublished = {\url{https://github.com/Trindad/graph-visual-rhythm}},
  month = {June},
  year = {2019}
}