Hi,
Here's an example:
Imaging your subgraph (let's call it sub. save it in a file called sub.gml)
looks like this in .gml format:
graph [
directed 1
node [
id 0
name "a"
status "down"
]
node [
id 1
name "b"
status "up"
]
]
And your larger graph (let's call it g. save it in a file called g.gml)
looks like this:
graph [
directed 1
node [
id 0
name "a"
status "up"
]
node [
id 1
name "b"
status "up"
]
node [
id 0
name "a"
status "down"
]
node [
id 1
name "b"
status "up"
]
node [
id 4
name "c"
status "down"
]
]
When you load your graphs, the properties load in the graphs' property maps
(you can retrieve them with g.vertex_properties['status'] for example).
Here's 2 ways you can find subgraphs that are isomorphic:
vertex_mapping_name, edge_mapping_name=gt.subgraph_isomorphism(sub, g,
vertex_label=(sub.vertex_properties["name"],g.vertex_properties["name"]),random=True)
vertex_mapping_status, edge_mapping_status=gt.subgraph_isomorphism(sub, g,
vertex_label=(sub.vertex_properties["status"],g.vertex_properties["status"]),random=True)
If I have multiple criteria, I perform an intersection of sorts after the
fact. I'm not sure if there's a better way to use different labels at the
same time.
I've pasted the python code below.
Hope that helps!
Reem
import graph_tool.all as gt
g = gt.load_graph("./g.gml")
sub = gt.load_graph("./sub.gml")
vertex_mapping_name, edge_mapping_name=gt.subgraph_isomorphism(sub, g,
vertex_label=(sub.vertex_properties["name"],g.vertex_properties["name"]),random=True)
vertex_mapping_status, edge_mapping_status=gt.subgraph_isomorphism(sub, g,
vertex_label=(sub.vertex_properties["status"],g.vertex_properties["status"]),random=True)
print(len(vertex_mapping_name))
print(len(vertex_mapping_status))
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