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- import networkx as nx
- import numpy as np
-
-
- def getSPLengths(G1):
- sp = nx.shortest_path(G1)
- distances = np.zeros((G1.number_of_nodes(), G1.number_of_nodes()))
- for i in np.keys():
- for j in np[i].keys():
- distances[i, j] = len(sp[i][j])-1
- return distances
-
- def getSPGraph(G):
- """Transform graph G to its corresponding shortest-paths graph.
-
- Parameters
- ----------
- G : NetworkX graph
- The graph to be tramsformed.
-
- Return
- ------
- S : NetworkX graph
- The shortest-paths graph corresponding to G.
-
- Notes
- ------
- For an input graph G, its corresponding shortest-paths graph S contains the same set of nodes as G, while there exists an edge between all nodes in S which are connected by a walk in G. Every edge in S between two nodes is labeled by the shortest distance between these two nodes.
-
- References
- ----------
- [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.
- """
- return floydTransformation(G)
-
- def floydTransformation(G):
- """Transform graph G to its corresponding shortest-paths graph using Floyd-transformation.
-
- Parameters
- ----------
- G : NetworkX graph
- The graph to be tramsformed.
-
- Return
- ------
- S : NetworkX graph
- The shortest-paths graph corresponding to G.
-
- References
- ----------
- [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.
- """
- spMatrix = nx.floyd_warshall_numpy(G) # @todo weigth label not considered
- S = nx.Graph()
- S.add_nodes_from(G.nodes(data=True))
- for i in range(0, G.number_of_nodes()):
- for j in range(0, G.number_of_nodes()):
- S.add_edge(i, j, cost = spMatrix[i, j])
- return S
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