Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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73 lines
2.1 KiB
73 lines
2.1 KiB
"""
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=====
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Atlas
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=====
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Atlas of all graphs of 6 nodes or less.
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"""
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import random
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# This example needs Graphviz and either PyGraphviz or pydot.
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# from networkx.drawing.nx_pydot import graphviz_layout
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from networkx.drawing.nx_agraph import graphviz_layout
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import matplotlib.pyplot as plt
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import networkx as nx
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from networkx.algorithms.isomorphism.isomorph import (
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graph_could_be_isomorphic as isomorphic,
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)
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from networkx.generators.atlas import graph_atlas_g
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def atlas6():
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""" Return the atlas of all connected graphs of 6 nodes or less.
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Attempt to check for isomorphisms and remove.
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"""
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Atlas = graph_atlas_g()[0:208] # 208
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# remove isolated nodes, only connected graphs are left
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U = nx.Graph() # graph for union of all graphs in atlas
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for G in Atlas:
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zerodegree = [n for n in G if G.degree(n) == 0]
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for n in zerodegree:
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G.remove_node(n)
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U = nx.disjoint_union(U, G)
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# iterator of graphs of all connected components
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C = (U.subgraph(c) for c in nx.connected_components(U))
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UU = nx.Graph()
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# do quick isomorphic-like check, not a true isomorphism checker
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nlist = [] # list of nonisomorphic graphs
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for G in C:
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# check against all nonisomorphic graphs so far
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if not iso(G, nlist):
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nlist.append(G)
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UU = nx.disjoint_union(UU, G) # union the nonisomorphic graphs
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return UU
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def iso(G1, glist):
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"""Quick and dirty nonisomorphism checker used to check isomorphisms."""
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for G2 in glist:
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if isomorphic(G1, G2):
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return True
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return False
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G = atlas6()
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print(f"graph has {nx.number_of_nodes(G)} nodes with {nx.number_of_edges(G)} edges")
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print(nx.number_connected_components(G), "connected components")
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plt.figure(1, figsize=(8, 8))
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# layout graphs with positions using graphviz neato
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pos = graphviz_layout(G, prog="neato")
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# color nodes the same in each connected subgraph
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C = (G.subgraph(c) for c in nx.connected_components(G))
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for g in C:
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c = [random.random()] * nx.number_of_nodes(g) # random color...
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nx.draw(g, pos, node_size=40, node_color=c, vmin=0.0, vmax=1.0, with_labels=False)
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plt.show()
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