/FGWD_on_Graphs_subgraph

MSc Thesis - Subgraph Matching via Fused Gromov-Wasserstein Distance

Primary LanguagePython

Subgraph Matching via Fused Gromov-Wasserstein Distance

MSc Thesis link here: http://resolver.tudelft.nl/uuid:d18d7083-4189-46a7-bd03-4cf49674cffe

Main references

Libraries

  • lib0: Implementation by definition (simple but computationally expensive)
  • lib1: Implementation with Peyre's trick (mostly used in the thesis)
  • lib2: Slight modification of lib1, specifically for KEGG database

Normalized Fused Gromov-Wasserstein (nFGW) Distance

  • subgraph_LargestValue: A simple example of the largest possible values (approximately 1.0) of nWD, nGWD, and nFGWD for subgraph matching

Also check FGWD_on_Graphs_basics for visualization of GW and FGW nonconvexity

Main results

Performance evaluation

Synthetic datasets: datasets contains 1000 pairs of randomly created query graph and test graphs

Real-world datasets

For G-Finder, two files are modified within the original project

Applications

Future directions

Searching for subgraphs of a different size of the query