The project implements a Weighted and directed graph model. The graph contains a data structure of a dictionary in a dictionary: the keys in the external dict are sources nodes keys, Every value is a pair (tuple) of (dest: weight), of an edge.
Graph is an important data structure studied in Computer Science. is an equally important topic in both mathematics and Computer Science.
Representing a graph in a program means finding a way to store the graph in a computer’s memory. Which is a prerequisite to working with graphs in…
Note
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An example using Graph as a weighted network.
elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] > 0.5] esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] <= 0.5] pos = nx.spring_layout(G, seed=7) # positions for all nodes - seed for reproducibility # nodes nx.draw_networkx_nodes(G, pos, node_size=700) # edges nx.draw_networkx_edges(G, pos, edgelist=elarge, width=6) nx.draw_networkx_edges( G, pos, edgelist=esmall, width=6, alpha=0.5, edge_color="b", style="dashed" ) # node labels nx.draw_networkx_labels(G, pos, font_size=20, font_family="sans-serif") # edge weight labels edge_labels = nx.get_edge_attributes(G, "weight") nx.draw_networkx_edge_labels(G, pos, edge_labels) ax = plt.gca() ax.margins(0.08) plt.axis("off") plt.tight_layout() plt.show()Total running time of the script: ( 0 minutes 0.081 seconds)
Download Python source code: plot_weighted_graph.py
Download Jupyter notebook: plot_weighted_graph.ipynb
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