Networkx - Subgraphs using node attributes
I have a set of data where the nodes have an attribute showing the name of the team to which they belong. I would like to work out the stats for a) the whole network and then b) the stats for each team comparing their connectivity etc. (Lets say I am comparing who sends emails to who and therefore the degree of connectedness of the team. Here is my sample code:
ST = nx.Graph()
ST.add_node('A',role = 'sales1')
ST.add_node('B',role = 'sales1')
ST.add_node('C',role = 'sales1')
ST.add_node('D',role = 'sales1')
ST.add_node('E',role = 'sales2')
ST.add_node('F',role = 'sales2')
ST.add_node('G',role = 'sales2')
ST.add_node('H',role = 'sales2')
ST.add_edges_from([('A','B'),
('A','C'),
('A','D'),
('B','D'),
('B','C'),
('C','D'),
('E','F'),
('E','G'),
('E','H'),
('F','H'),
('G','H'),
('A','E'),
('G','D')])
I know I can form a subgraph as follows:
H = ST.subgraph(['A','B','C','D'])
Is there an easier way of setting the nodes by reference to the node attribute?
python networkx
asked Nov 15 '18 at 12:25
RobHRobH
32
add a comment |
I have a set of data where the nodes have an attribute showing the name of the team to which they belong. I would like to work out the stats for a) the whole network and then b) the stats for each team comparing their connectivity etc. (Lets say I am comparing who sends emails to who and therefore the degree of connectedness of the team. Here is my sample code:
ST = nx.Graph()
ST.add_node('A',role = 'sales1')
ST.add_node('B',role = 'sales1')
ST.add_node('C',role = 'sales1')
ST.add_node('D',role = 'sales1')
ST.add_node('E',role = 'sales2')
ST.add_node('F',role = 'sales2')
ST.add_node('G',role = 'sales2')
ST.add_node('H',role = 'sales2')
ST.add_edges_from([('A','B'),
('A','C'),
('A','D'),
('B','D'),
('B','C'),
('C','D'),
('E','F'),
('E','G'),
('E','H'),
('F','H'),
('G','H'),
('A','E'),
('G','D')])
I know I can form a subgraph as follows:
H = ST.subgraph(['A','B','C','D'])
Is there an easier way of setting the nodes by reference to the node attribute?
python networkx
asked Nov 15 '18 at 12:25
RobHRobH
32
add a comment |
I have a set of data where the nodes have an attribute showing the name of the team to which they belong. I would like to work out the stats for a) the whole network and then b) the stats for each team comparing their connectivity etc. (Lets say I am comparing who sends emails to who and therefore the degree of connectedness of the team. Here is my sample code:
ST = nx.Graph()
ST.add_node('A',role = 'sales1')
ST.add_node('B',role = 'sales1')
ST.add_node('C',role = 'sales1')
ST.add_node('D',role = 'sales1')
ST.add_node('E',role = 'sales2')
ST.add_node('F',role = 'sales2')
ST.add_node('G',role = 'sales2')
ST.add_node('H',role = 'sales2')
ST.add_edges_from([('A','B'),
('A','C'),
('A','D'),
('B','D'),
('B','C'),
('C','D'),
('E','F'),
('E','G'),
('E','H'),
('F','H'),
('G','H'),
('A','E'),
('G','D')])
I know I can form a subgraph as follows:
H = ST.subgraph(['A','B','C','D'])
Is there an easier way of setting the nodes by reference to the node attribute?
python networkx
asked Nov 15 '18 at 12:25
RobHRobH
32
I have a set of data where the nodes have an attribute showing the name of the team to which they belong. I would like to work out the stats for a) the whole network and then b) the stats for each team comparing their connectivity etc. (Lets say I am comparing who sends emails to who and therefore the degree of connectedness of the team. Here is my sample code:
ST = nx.Graph()
ST.add_node('A',role = 'sales1')
ST.add_node('B',role = 'sales1')
ST.add_node('C',role = 'sales1')
ST.add_node('D',role = 'sales1')
ST.add_node('E',role = 'sales2')
ST.add_node('F',role = 'sales2')
ST.add_node('G',role = 'sales2')
ST.add_node('H',role = 'sales2')
ST.add_edges_from([('A','B'),
('A','C'),
('A','D'),
('B','D'),
('B','C'),
('C','D'),
('E','F'),
('E','G'),
('E','H'),
('F','H'),
('G','H'),
('A','E'),
('G','D')])
I know I can form a subgraph as follows:
H = ST.subgraph(['A','B','C','D'])
Is there an easier way of setting the nodes by reference to the node attribute?
python networkx
python networkx
asked Nov 15 '18 at 12:25
RobHRobH
32
asked Nov 15 '18 at 12:25
RobHRobH
32
asked Nov 15 '18 at 12:25
RobHRobH
32
asked Nov 15 '18 at 12:25
RobHRobH
32
asked Nov 15 '18 at 12:25
RobHRobH
32
32
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
You can use itertools.groupby:
from itertools import groupby
# Sort the nodes by their role (this is required for the groupby to work)
sorted_by_role = sorted(ST.nodes(data=True), key=lambda node_data: node_data[1]["role"])
# Group objects with same role together
grouped = groupby(sorted_by_role, key=lambda node_data: node_data[1]["role"])
subgraphs = dict()
for key, group in grouped:
nodes_in_group, _ = zip(*list(group)) # We don't care about the data here, only the node names
subgraphs[key] = ST.subgraph(nodes_in_group)
Now the subgraphs dict contains the subgraph of every different role present in the graph. This works for an arbitrary number of roles.
answered Nov 15 '18 at 12:43
RunOrVeithRunOrVeith
1,1971223
thank you. That works a treat!
– RobH
Nov 15 '18 at 14:13
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You can use itertools.groupby:
from itertools import groupby
# Sort the nodes by their role (this is required for the groupby to work)
sorted_by_role = sorted(ST.nodes(data=True), key=lambda node_data: node_data[1]["role"])
# Group objects with same role together
grouped = groupby(sorted_by_role, key=lambda node_data: node_data[1]["role"])
subgraphs = dict()
for key, group in grouped:
nodes_in_group, _ = zip(*list(group)) # We don't care about the data here, only the node names
subgraphs[key] = ST.subgraph(nodes_in_group)
Now the subgraphs dict contains the subgraph of every different role present in the graph. This works for an arbitrary number of roles.
answered Nov 15 '18 at 12:43
RunOrVeithRunOrVeith
1,1971223
thank you. That works a treat!
– RobH
Nov 15 '18 at 14:13
add a comment |
You can use itertools.groupby:
from itertools import groupby
# Sort the nodes by their role (this is required for the groupby to work)
sorted_by_role = sorted(ST.nodes(data=True), key=lambda node_data: node_data[1]["role"])
# Group objects with same role together
grouped = groupby(sorted_by_role, key=lambda node_data: node_data[1]["role"])
subgraphs = dict()
for key, group in grouped:
nodes_in_group, _ = zip(*list(group)) # We don't care about the data here, only the node names
subgraphs[key] = ST.subgraph(nodes_in_group)
Now the subgraphs dict contains the subgraph of every different role present in the graph. This works for an arbitrary number of roles.
answered Nov 15 '18 at 12:43
RunOrVeithRunOrVeith
1,1971223
thank you. That works a treat!
– RobH
Nov 15 '18 at 14:13
add a comment |
You can use itertools.groupby:
from itertools import groupby
# Sort the nodes by their role (this is required for the groupby to work)
sorted_by_role = sorted(ST.nodes(data=True), key=lambda node_data: node_data[1]["role"])
# Group objects with same role together
grouped = groupby(sorted_by_role, key=lambda node_data: node_data[1]["role"])
subgraphs = dict()
for key, group in grouped:
nodes_in_group, _ = zip(*list(group)) # We don't care about the data here, only the node names
subgraphs[key] = ST.subgraph(nodes_in_group)
Now the subgraphs dict contains the subgraph of every different role present in the graph. This works for an arbitrary number of roles.
answered Nov 15 '18 at 12:43
RunOrVeithRunOrVeith
1,1971223
You can use itertools.groupby:
from itertools import groupby
# Sort the nodes by their role (this is required for the groupby to work)
sorted_by_role = sorted(ST.nodes(data=True), key=lambda node_data: node_data[1]["role"])
# Group objects with same role together
grouped = groupby(sorted_by_role, key=lambda node_data: node_data[1]["role"])
subgraphs = dict()
for key, group in grouped:
nodes_in_group, _ = zip(*list(group)) # We don't care about the data here, only the node names
subgraphs[key] = ST.subgraph(nodes_in_group)
Now the subgraphs dict contains the subgraph of every different role present in the graph. This works for an arbitrary number of roles.
answered Nov 15 '18 at 12:43
RunOrVeithRunOrVeith
1,1971223
answered Nov 15 '18 at 12:43
RunOrVeithRunOrVeith
1,1971223
answered Nov 15 '18 at 12:43
RunOrVeithRunOrVeith
1,1971223
answered Nov 15 '18 at 12:43
RunOrVeithRunOrVeith
1,1971223
1,1971223
thank you. That works a treat!
– RobH
Nov 15 '18 at 14:13
add a comment |
thank you. That works a treat!
– RobH
Nov 15 '18 at 14:13
thank you. That works a treat!
– RobH
Nov 15 '18 at 14:13
thank you. That works a treat!
– RobH
Nov 15 '18 at 14:13
add a comment |
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