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.
- Python 3.9
- Numpy
- A C++ compatible compiler
# 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 .python -m pytest tests# 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)[...]
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)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}
}
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