I have bipartite graph with nodes such as(a1,a2,...a100, m1,m2,...). I want to find the induced subgraph for certain nodes say(a1,a2 and a10). I can do this by using networkx.ego_graph
, but it takes one vertex at one time and returns the induced graph. I want to know if there is any way to do this at once for all the nodes that i am interested in and then select the one that is largest.
An ego graph is the graph of all nodes that are less than a certain distance from the tensorflow node.
An ego network is defined as a portion of a social network formed of a given individual, termed ego, and the other persons with whom she has a social relationship, termed alters.
NX is certainly capable of handling graphs that large, however, performance will largely be a function of your hardware setup. Aric will likely give a better answer, but NX loads graphs into memory at once, so in the ranges your are describing you will need a substantial amount of free memory for it to work.
For the general case, the ego graph can be obtained using nx.ego_graph
.
Though in your specific case, it looks like you want to find the largest induced ego graph
in the network. For that you can first find the node with a highest degree, and then obtain its ego graph.
Let's create an example bipartite graph:
import networkx as nx
B = nx.Graph()
B.add_nodes_from([1, 2, 3, 4, 5, 6], bipartite=0)
B.add_nodes_from(['a', 'b', 'c', 'j', 'k'], bipartite=1)
B.add_edges_from([(1, 'a'), (1, 'b'), (2, 'b'), (2, 'c'), (3, 'c'), (4, 'a'),
(2, 'b'), (3, 'a'), (5, 'k'), (6, 'k'), (6, 'j')])
rcParams['figure.figsize'] = 12, 6
nx.draw(B, node_color='lightblue',
with_labels=True)
And as mentioned in the question, say we want to select among the following list of nodes:
l = [1,'a',6]
It looks like you want to select the one that has the highest centrality degree among these. For that you could do:
deg_l = {i:B.degree(i) for i in l}
highest_centrality_node = max(deg_l.items(), key=lambda x: x[1])[0]
Now we could plot the corresponding ego_graph
with:
ego_g = nx.ego_graph(B, highest_centrality_node)
d = dict(ego_g.degree)
nx.draw(ego_g, node_color='lightblue',
with_labels=True,
nodelist=d,
node_size=[d[k]*300 for k in d])
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