- import networkx as nx
- import numpy as np
-
- # from tqdm import tqdm
-
-
- def getSPLengths(G1):
- sp = nx.shortest_path(G1)
- distances = np.zeros((G1.number_of_nodes(), G1.number_of_nodes()))
- for i in sp.keys():
- for j in sp[i].keys():
- distances[i, j] = len(sp[i][j]) - 1
- return distances
-
-
- def getSPGraph(G, edge_weight='bond_type'):
- """Transform graph G to its corresponding shortest-paths graph.
-
- Parameters
- ----------
- G : NetworkX graph
- The graph to be tramsformed.
- edge_weight : string
- edge attribute corresponding to the edge weight. The default edge weight is bond_type.
-
- 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, edge_weight=edge_weight)
-
-
- def floydTransformation(G, edge_weight='bond_type'):
- """Transform graph G to its corresponding shortest-paths graph using Floyd-transformation.
-
- Parameters
- ----------
- G : NetworkX graph
- The graph to be tramsformed.
- edge_weight : string
- edge attribute corresponding to the edge weight. The default edge weight is bond_type.
-
- 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, weight=edge_weight)
- S = nx.Graph()
- S.add_nodes_from(G.nodes(data=True))
- for i in range(0, G.number_of_nodes()):
- for j in range(i, G.number_of_nodes()):
- S.add_edge(i, j, cost=spMatrix[i, j])
- return S
-
-
- def untotterTransformation(G, node_label, edge_label):
- """Transform graph G according to Mahé et al.'s work to filter out tottering patterns of marginalized kernel and tree pattern kernel.
-
- Parameters
- ----------
- G : NetworkX graph
- The graph to be tramsformed.
- node_label : string
- node attribute used as label. The default node label is 'atom'.
- edge_label : string
- edge attribute used as label. The default edge label is 'bond_type'.
-
- Return
- ------
- gt : NetworkX graph
- The shortest-paths graph corresponding to G.
-
- References
- ----------
- [1] Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert. Extensions of marginalized graph kernels. In Proceedings of the twenty-first international conference on Machine learning, page 70. ACM, 2004.
- """
- # arrange all graphs in a list
- G = G.to_directed()
- gt = nx.Graph()
- gt.graph = G.graph
- gt.add_nodes_from(G.nodes(data=True))
- for edge in G.edges():
- gt.add_node(edge)
- gt.node[edge].update({node_label: G.node[edge[1]][node_label]})
- gt.add_edge(edge[0], edge)
- gt.edges[edge[0], edge].update({
- edge_label:
- G[edge[0]][edge[1]][edge_label]
- })
- for neighbor in G[edge[1]]:
- if neighbor != edge[0]:
- gt.add_edge(edge, (edge[1], neighbor))
- gt.edges[edge, (edge[1], neighbor)].update({
- edge_label:
- G[edge[1]][neighbor][edge_label]
- })
- # nx.draw_networkx(gt)
- # plt.show()
-
- # relabel nodes using consecutive integers for convenience of kernel calculation.
- gt = nx.convert_node_labels_to_integers(
- gt, first_label=0, label_attribute='label_orignal')
- return gt
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