cyged 0.2.3

A python module to compute GED using cython

Graph Matching Framework

This is a Cython framework to perform graph matching. It uses the flexibility of python and the efficiency of compiled c code to package contains the core files to perform graph matching.

Installation

Prerequisites

• Python 3.9
• Numpy
• A C++ compatible compiler

Install

# Clone repo
git clone https://github.com/CheshireCat12/graph-matching-core.git [folder-name]
# Move to repo directory
cd [folder-name]

#Create python virtual environment with python
python -m venv venv
source venv/bin/activate

# Install Numpy
pip install numpy

# Compile and install the code
pip install -e .

Run tests

python -m pytest tests

How to use

# More example available in ./tests/

from cyged.graph_pkg_core import GED
from cyged.graph_pkg_core.edit_cost.edit_cost_vector import EditCostVector
from cyged.graph_pkg_core.graph.edge import Edge
from cyged.graph_pkg_core import Graph
from cyged.graph_pkg_core.graph.label.label_edge import LabelEdge
from cyged.graph_pkg_core.graph.label.label_node_vector import LabelNodeVector
from cyged.graph_pkg_core.graph.node import Node
from cyged.graph_pkg_core import LoaderVector

ged = GED(EditCostVector(1., 1., 1., 1., 'euclidean'))

n, m = 4, 3
graph_source = Graph('gr_source', 'gr_source.gxl', n)
graph_target = Graph('gr_target', 'gr_targe.gxl', m)

# Init graph source: add nodes and edges
graph_source.add_node(Node(0, LabelNodeVector(np.array([1.]))))
graph_source.add_node(Node(1, LabelNodeVector(np.array([2.]))))
graph_source.add_node(Node(2, LabelNodeVector(np.array([1.]))))
graph_source.add_node(Node(3, LabelNodeVector(np.array([3.]))))
graph_source.add_edge(Edge(0, 1, LabelEdge(0)))
graph_source.add_edge(Edge(1, 2, LabelEdge(0)))
graph_source.add_edge(Edge(1, 3, LabelEdge(0)))
graph_source.add_edge(Edge(2, 3, LabelEdge(0)))

# Init graph target: add nodes and edges
graph_target.add_node(Node(0, LabelNodeVector(np.array([3.]))))
graph_target.add_node(Node(1, LabelNodeVector(np.array([2.]))))
graph_target.add_node(Node(2, LabelNodeVector(np.array([2.]))))
graph_target.add_edge(Edge(0, 1, LabelEdge(0)))
graph_target.add_edge(Edge(1, 2, LabelEdge(0)))

edit_cost = ged.compute_edit_distance(graph_source, graph_target)

Parallelization

[...]
from cyged.graph_pkg_core.algorithm.matrix_distances import MatrixDistances

ged = GED(EditCostVector(1., 1., 1., 1., 'euclidean'))

# List of graphs
source_graphs = [grs1, grs2, ..., grsm]
target_graphs = [grt1, grt2, ..., grtn]

mat_dist = MatrixDistances(ged, parallel=True)

mat_edit_dist = mat_dist.calc_matrix_distances(source_graphs,
                                               target_graphs,
                                               heuristic=True,
                                               num_cores=8)

Cite

Please cite our paper if you use this code in your work:

@inproceedings{GilliozR22,
  author    = {Anthony Gillioz and
               Kaspar Riesen},
  editor    = {Maria De Marsico and
               Gabriella Sanniti di Baja and
               Ana L. N. Fred},
  title     = {Improving Graph Classification by Means of Linear Combinations of
               Reduced Graphs},
  booktitle = {Proceedings of the 11th International Conference on Pattern Recognition
               Applications and Methods, {ICPRAM} 2022, Online Streaming, February
               3-5, 2022},
  pages     = {17--23},
  publisher = {{SCITEPRESS}},
  year      = {2022},
  url       = {https://doi.org/10.5220/0010776900003122},
  doi       = {10.5220/0010776900003122}
}