Data underlying the publication: Graph Coarsening for Fugitive Interception

doi:10.4121/07643762-6038-4ccc-bf94-4bf56b5abeae.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/07643762-6038-4ccc-bf94-4bf56b5abeae
Datacite citation style:
van Droffelaar, Irene (2024): Data underlying the publication: Graph Coarsening for Fugitive Interception. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/07643762-6038-4ccc-bf94-4bf56b5abeae.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

This repository is part of the Ph.D. thesis of Irene S. van Droffelaar, Delft University of Technology.


coarseningFIP.zip contains all algorithms and data accompanying the paper "Graph Coarsening for Fugitive Interception":

  • Directories pruning, consolidate_nodes, heuristic, and onthefly contain the algorithms for graph coarsening. The heuristic coarsening algorithm is the Python implementation of the algorithm proposed by Krishnakumari et al. (2020) and can also be found in heuristic_coarsening.zip and https://github.com/irene-sophia/HeuristicCoarsening.
  • The directory HPC_results contains the results of the experiments for all five road networks for all algorithms.
  • The directory analysis contains the notebooks that analyze the experiments, also including the cross-evaluation, counting the numbers of nodes in each network, and the timing experiments. Most plots in the paper are generated in the compare_methods.ipynb notebook.
  • The directory data contains the coarsened networks resulting from each algorithm, and the simulated routes.
  • The directory route_simulation contains the route generation code.


platypus-fork.zip contains the optimization algorithm.


history
  • 2024-11-25 first online, published, posted
publisher
4TU.ResearchData
format
.py, .ipynb, .csv, .pkl, .graphml
funding
  • National Police Artificial Intelligence Lab National Police Artificial Intelligence Lab
organizations
TU Delft, Faculty of Technology, Policy and Management, Department of Multi-Actor Systems (MAS)

DATA

files (3)