### Improve documentation of subgraph_isomorphism() and motifs()

parent 6fcf0f1b
 ... ... @@ -286,9 +286,9 @@ def extended_clustering(g, props=None, max_depth=3, undirected=False): def motifs(g, k, p=1.0, motif_list=None, return_maps=False): r""" Count the occurrence of k-size subgraphs (motifs). A tuple with two lists is returned: the list of motifs found, and the list with their respective counts. Count the occurrence of k-size node-induced subgraphs (motifs). A tuple with two lists is returned: the list of motifs found, and the list with their respective counts. Parameters ---------- ... ... @@ -358,6 +358,7 @@ def motifs(g, k, p=1.0, motif_list=None, return_maps=False): motifs", IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 3, Issue 4, Pages 347-359, 2006. :doi:10.1109/TCBB.2006.51 .. [induced-subgraph-isomorphism] http://en.wikipedia.org/wiki/Induced_subgraph_isomorphism_problem """ sub_list = [] ... ...
 ... ... @@ -222,6 +222,11 @@ def subgraph_isomorphism(sub, g, max_n=0, vertex_label=None, edge_label=None, Notes ----- Here "subgraph" does not mean "node-induced subgraph", i.e. there may exist an edge in the matched subgraph in g that does not exist in sub. For node-induced subgraph isomorphism, see the :func:+graph_tool.clustering.motifs function. The algorithm used is described in [ullmann-algorithm-1976]_. It has a worse-case complexity of :math:O(N_g^{N_{sub}}), but for random graphs it typically has a complexity of :math:O(N_g^\gamma) with :math:\gamma ... ...
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