Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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49 lines
986 B
49 lines
986 B
4 years ago
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"""
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==========
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Properties
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==========
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Compute some network properties for the lollipop graph.
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"""
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import matplotlib.pyplot as plt
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from networkx import nx
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G = nx.lollipop_graph(4, 6)
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pathlengths = []
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print("source vertex {target:length, }")
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for v in G.nodes():
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spl = dict(nx.single_source_shortest_path_length(G, v))
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print(f"{v} {spl} ")
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for p in spl:
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pathlengths.append(spl[p])
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print()
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print(f"average shortest path length {sum(pathlengths) / len(pathlengths)}")
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# histogram of path lengths
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dist = {}
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for p in pathlengths:
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if p in dist:
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dist[p] += 1
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else:
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dist[p] = 1
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print()
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print("length #paths")
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verts = dist.keys()
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for d in sorted(verts):
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print(f"{d} {dist[d]}")
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print(f"radius: {nx.radius(G)}")
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print(f"diameter: {nx.diameter(G)}")
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print(f"eccentricity: {nx.eccentricity(G)}")
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print(f"center: {nx.center(G)}")
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print(f"periphery: {nx.periphery(G)}")
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print(f"density: {nx.density(G)}")
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nx.draw(G, with_labels=True)
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plt.show()
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