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Build a supergraph of a dataset

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Supergraphs

Implementation of the method described in Local Patterns and Supergraph for Chemical Graph Classification with Convolutional Networks (S+SSPR2018, pdf here).

List of executables

  • bin/supergraph : compute a supergraph of a graph database as well as the projections of input data onto the supergraph
  • bin/supergraph-cv : compute a supergraph for each cross-validation subset
  • bin/supergraph-cv-omp : the same, multi-threaded with OpenMP
  • bin/project : compute the projections of the database's graphs onto a supergraph
  • bin/stars : compute stars present in a graph database encoded as a vector
  • bin/ds2json : convert a .ds dataset into json format
  • bin/ds_stats : compute stats of a dataset
  • bin/cv_indices : create files of indices for each cross-validation subset

It is possible to create the program bin/supergraph-cv-omp using a bipartite approximation of graph edit distance at each step of the algorithm by using the rule supergraph-cv-bipartite of the Makefile. This accelerates the computation but the resulting supergraph will be more dense. For more information about this, please consult the paper.

Requirements

lsape, graph_lib and edmonds

lsape is a toolbox to address the linear sum assignment problem with edition (Greyc lab).
graph_lib is toolbox of graph algorithms, especially adressing the problem of Graph Edit Distance estimation (Greyc lab).
edmonds is an implementation of the Edmonds-Blossom algorithm to find minimum weight perfect matchings on graphs.

* lsape                 https://bougleux.users.greyc.fr/lsape
* graph_lib             https://github.com/bgauzere/graph-lib
* edmonds               https://github.com/lacop/edmonds

It is then necessary to update the global variables in the Makefile :

  • LSAPE_DIR Path to the repertory include of lsape
  • GRAPH_DIR Path to the repertory include of graph_lib
  • GRAPH_OBJ Path to the file graphlib.a
  • EDMONDS_DIR Path to the root repertory of edmonds

for graph_lib dependences are :

  • LSAPE
  • TinyXML
  • Eigen3
  • OpenMP (for the multi-thread version)

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