Is there a NetworkX function to find the k-nearest nodes of 0-degree nodes

Question

I am trying to connect the centroids of buildings (and other areas) to a NetworkX.MultiGraph representing the road network. These centroids are all 0-degree nodes.

I have read posts on finding the nearest neighbors in the same graph but failed to find anything for ‘off-grid’ nodes. Graph.neighbors() and ego_graph() all returned empty iterables for the centroid nodes.

I wonder if there is a function for this case. Do I have to iterate over all the nodes on the roads to compute their distances to the centroid? Or there is a better idea?

The Graph

The red dots are the nodes to connect.

Minimal example data

some_edges = [((12965572.317313856, 4069201.5797910197),
  (12965582.853703659, 4069201.5541923903)),
 ((12965582.853703659, 4069201.5541923903),
  (12965593.39009346, 4069201.528593761)),
 ((12965593.39009346, 4069201.528593761),
  (12965637.645157026, 4069201.426199232)),
 ((12965593.39009346, 4069201.528593761),
  (12965594.291781338, 4069120.115297204)),
 ((12965593.39009346, 4069201.528593761),
  (12965593.790843628, 4069233.4285842725))]

a_centroid = [((12965783.891266523, 4069098.4402361237), {'id': 839812059, 'node_type': 'area_centroid', 'amenity': 'library', 'building': 'yes', 'name': '逸夫图书馆', 'name:zh': '逸夫图书馆', 'name:zh-Hans': '逸夫图书馆', 'name:zh-Hant': '逸夫圖書館', 'operator': '北京工业大学'})]
G = nx.MultiGraph(some_edges)
G.add_nodes_from(a_centroid)

color_map = ['red' if 'name' in attr.keys() else 'blue' for n, attr in G.nodes.data()]
nx.draw(G, {n: (n[0], n[1]) for n in G.nodes()}, node_color=color_map, ax=plt.gca())

should produce a minimal example with a graph like this:

where the blue nodes are parts of the graph while the red node is a centroid to connect (to its nearest node in the graph).

Asked By: Navxihziq

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Source

Answer 1

To be simple, you can use KDTree from scipy.spatial package:

from scipy.spatial import KDTree

# Separate orphan nodes (red) from road nodes (blue)
orphans = [node for node, degree in G.degree if degree == 0]
nodes = [node for node, degree in G.degree if degree > 0]

# Find the nearest blue node to each red node
distances, indices = KDTree(nodes).query(orphans, k=1)

# Build new edges to join red nodes
edges = [(orphan, nodes[index]) for orphan, index in zip(orphans, indices)]
G.add_edges_from(edges)

Output:

>>> edges
[((12965783.891266523, 4069098.4402361237), (12965637.645157026, 4069201.426199232))]

Answered By: Corralien

Is there a NetworkX function to find the k-nearest nodes of 0-degree nodes

Question:

Minimal example data

Answers: