Get networkx filtered nodes from subgraph_view of filtered edges - networkx

Question

I've created a subgraph_view by applying a filter to edges. When I call nodes() on the subgraph it still shows me all nodes, even if none of the edges use them. I need to get a list of only nodes that are still part of the subgraph.
G = nx.path_graph(6)
G[2][3]["cross_me"] = False
G[3][4]["cross_me"] = False
def filter_edge(n1, n2):
return G[n1][n2].get("cross_me", True)
view = nx.subgraph_view(G, filter_edge=filter_edge)
# node 3 is no longer used by any edges in the subgraph
view.edges()
This produces
EdgeView([(0, 1), (1, 2), (4, 5)])
as expected. However, when I run view.nodes() I get
NodeView((0, 1, 2, 3, 4, 5))
What I expect to see is
NodeView((0, 1, 2, 4, 5))
This seems odd. Is there some way to extract only the nodes used by the subgraph?

Answer 1

The confusion stems from the definition of 'graph.' A disconnected node is still a part of a graph. In fact, you could have a graph with no edges at all. So the behavior of subgraph_view() is counterintuitive but correct.
If, however, you still want to achieve what you're describing, there are lots of potential ways, depending on your tolerance for modifying the original graph. I'll mention two that attempt to stay as close to your current method as possible and avoid deleting edges or nodes from G.
Method 1
The easiest way using your view object is to take it as input to edge_subgraph() (which only takes edges as input) like this:
final_view = view.edge_subgraph(view.edges())
final_view.nodes()
gives
NodeView((0, 1, 2, 4, 5))
Method 2
To me, Method 1 seems clunky and confusing by defining an intermediate view. If instead we go back up a little bit and start with G, we could define a filter_node function that checks the edge attributes of each node and filters that node if
all edges are flagged for removal, or
the node has no edges in the first place.
You could also do this by manually flagging the node itself, as you've done with the edges.
G = nx.path_graph(6)
G[2][3]["cross_me"] = False
G[3][4]["cross_me"] = False
def filter_edge(n1, n2):
return G[n1][n2].get("cross_me", True)
def filter_node(n):
return sum([i[2].get("cross_me", True) for i in G.edges(n, data=True)])
view = nx.subgraph_view(G, filter_node=filter_node, filter_edge=filter_edge)
view.nodes()
also gives the expected
NodeView((0, 1, 2, 4, 5))