Hi,
I was wondering if there is any way to assign vertex properties while adding edges to the graph. for example using "add_edge_list" I can assign edge properties but later I have to iterate through all vertices again to assign their properties.
I know this is not a problem when the vertex property is of the type "int" or "float" because then one can use "vprop.a = values", but in case of "string" and "object" this method doesn't work
What would be the best/fastest way to handle this situation.
I guess it would be very helpful to extend the "add_edge_list" function to accept vertex property in some way.
cheers,
On 29.07.2016 09:57, m jadidi wrote:
Hi,
I was wondering if there is any way to assign vertex properties while adding edges to the graph. for example using "add_edge_list" I can assign edge properties but later I have to iterate through all vertices again to assign their properties.
I know this is not a problem when the vertex property is of the type "int" or "float" because then one can use "vprop.a = values", but in case of "string" and "object" this method doesn't work
What would be the best/fastest way to handle this situation.
You can create new property maps with initialized values of any kind with
p = g.new_vertex_property("string", vals=values)
Best, Tiago
Simple example using add_edge_list()
edges = [["A", "B", 10], ["A", "C", 10], ["B", "C", 10], ["C", "D", 1], ["B", "F", 1], ["A", "E", 1], ["D", "E", 10], ["D", "F", 10], ["E", "F", 10]]
g = Graph() eweight = g.new_edge_property("int") eprops = [eweight] g.add_edge_list(edges, eprops=eprops, hashed=True)
How do I recover the vertex names ("A", "B", "C", etc), so that after I fit an SBM I can collect membership of vertices?
Now, if I had loaded the same data set from a .csv using load_graph_from_csv() and run this:
g = load_graph_from_csv('edges.csv', hashed = True)
Then the following keep track of both edge weights and vertex names:
weights = g.edge_properties['c1'] vnames = g.vertex_properties
However, if I fit an SBM to g using edge weights using this method, I get an error message:
AttributeError: 'str' object has no attribute 'key_type'
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Am 04.03.20 um 15:26 schrieb sam:
Simple example using add_edge_list()
edges = [["A", "B", 10], ["A", "C", 10], ["B", "C", 10], ["C", "D", 1], ["B", "F", 1], ["A", "E", 1], ["D", "E", 10], ["D", "F", 10], ["E", "F", 10]]
g = Graph() eweight = g.new_edge_property("int") eprops = [eweight] g.add_edge_list(edges, eprops=eprops, hashed=True)
How do I recover the vertex names ("A", "B", "C", etc), so that after I fit an SBM I can collect membership of vertices?
Read the manual! The docstring of g.add_edge_list() explains exactly this, i.e. a property map is returned which contains the names.
Now, if I had loaded the same data set from a .csv using load_graph_from_csv() and run this:
g = load_graph_from_csv('edges.csv', hashed = True)
Then the following keep track of both edge weights and vertex names:
weights = g.edge_properties['c1'] vnames = g.vertex_properties
The above is obviously not what you intended, as vnames points to the whole property dictionary, not any particular property map.
However, if I fit an SBM to g using edge weights using this method, I get an error message:
AttributeError: 'str' object has no attribute 'key_type'
Since you have not specified what you have actually done by showing us the code, it's difficult to say what the problem is. Most likely, the edge property map passed had the incorrect type.
If you want us to help you, please remember to always provide a minimal and self-contained example that shows the problem. Giving error messages without the context tells us almost nothing.
Best, Tiago
Tiago Peixoto wrote
The docstring of g.add_edge_list() explains exactly this, i.e. a property map is returned which contains the names.
So when you run g.add_edge_list() with the hashed = True option, it adds the edges to the graph and returns a vertex property map.
For anyone else as clueless as me that might read this in the future, here's the code:
As for the .csv method, here's what the .csv file looks like
and here's the code:
It seems to work!
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m jadidi -
sam -
Tiago de Paula Peixoto