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Centrality – Python - NetworkX

Centrality – Python - NetworkX. Degree Centrality. Degree Centrality – Find the “Celebrities” Figure 1.1 Network >>> degree_centrality = net.degree_centrality(g) >>> degree_centrality {1: 0.375, 2: 0.25, 3: 0.375, 4: 0.5, 5: 0.5, 6: 0.5, 7: 0.5, 8: 0.375, 9: 0.125}

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Centrality – Python - NetworkX

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  1. Centrality – Python - NetworkX LINK@KOREATECH

  2. Degree Centrality • Degree Centrality – Find the “Celebrities” • Figure 1.1 Network • >>> degree_centrality = net.degree_centrality(g) • >>> degree_centrality • {1: 0.375, 2: 0.25, 3: 0.375, 4: 0.5, 5: 0.5, 6: 0.5, 7: 0.5, 8: 0.375, 9: 0.125} • Definesort_map funtion • Sort the degree centrality • >>> import sort_map • >>> sorted_degree_centrality = sort_map.sort_map(degree_centrality) • >>> sorted_degree_centrality • [(4, 0.5), (5, 0.5), (6, 0.5), (7, 0.5), (1, 0.375), (3, 0.375), (8, 0.375), (2, 0.25), (9, 0.125)] sort_map.py • import operator • def sort_map(map): • sortedList = map.items() • sortedList.sort(key=operator.itemgetter(1), reverse=True) • return sortedList LINK@KOREATECH

  3. Closeness Centrality • Closeness Centrality - Find the Gossipmongers • Figure 1.1 Network • >>> closeness_centrality = net.closeness_centrality(g) • >>> closeness_centrality • {1: 0.47058823529411764, 2: 0.34782608695652173, 3: 0.47058823529411764, 4: 0.6153846153846154, 5: 0.6153846153846154, 6: 0.6153846153846154, 7: 0.5,8: 0.47058823529411764, 9: 0.34782608695652173} • >>> import sort_map • >>> sorted_closeness_centrality = sort_map.sort_map(closeness_centrality) • >>> sorted_closeness_centrality • [(4, 0.6153846153846154), (5, 0.6153846153846154), (6, 0.6153846153846154), (7, 0.5), (1, 0.47058823529411764), (3, 0.47058823529411764), (8, 0.47058823529411764), (2, 0.34782608695652173), (9, 0.34782608695652173)] LINK@KOREATECH

  4. Betweenness Centrality • Betweenness Centrality - Find the Communication Bottlenecks and/or Community Bridges • Figure 1.1 Network • >>> bet_centrality = net.betweenness_centrality(g) • >>> bet_centrality • {1: 0.10714285714285714, 2: 0.0, 3: 0.10714285714285714, 4: 0.5357142857142857, 5: 0.21428571428571427, 6: 0.21428571428571427, 7: 0.25, 8: 0.0, 9: 0.0} • >>> import sort_map • >>> sorted_bet_centrality = sort_map.sort_map(bet_centrality) • >>> sorted_bet_centrality • [(4, 0.5357142857142857), (7, 0.25), (5, 0.21428571428571427), (6, 0.21428571428571427), (1, 0.10714285714285714), (3, 0.10714285714285714), (2, 0.0), (8, 0.0), (9, 0.0)] LINK@KOREATECH

  5. Eigenvector Centrality • Eigenvector Centrality • Figure 1.1 Network • >>> eigenvector_centrality = net. eigenvector_centrality(g) • >>> eigenvector_centrality • {1: 0.1957517982158921, 2: 0.11168619729756277, 3: 0.1957517982158921, 4: 0.3787497778502688, 5: 0.4680845766467969, 6: 0.4680845766467969, 7: 0.4099777873648637, 8: 0.3840189757284676, 9: 0.11695539517576158} • >>> import sort_map • >>> sorted_eigenvector_centrality = sort_map.sort_map(eigenvector_centrality) • >>> sorted_eigenvector_centrality • [(5, 0.4680845766467969), (6, 0.4680845766467969), (7, 0.4099777873648637), (8, 0.3840189757284676), (4, 0.3787497778502688), (1, 0.1957517982158921), (3, 0.1957517982158921), (9, 0.11695539517576158), (2, 0.11168619729756277)] LINK@KOREATECH

  6. Putting It Together • Adjust the values • Values Rounded • >>> rounded_degree_centrality = {k: round(v, 3) for k, v in degree_centrality.items()} • >>> rounded_closeness_centrality = {k: round(v, 3) for k, v in closeness_centrality.items()} • >>> rounded_bet_centrality = {k: round(v, 3) for k, v in bet_centrality.items()} • >>> rounded_eigenvector_centrality = {k: round(v, 3) for k, v in eigenvector_centrality.items()} • Build a table with four centralities • Making some conclusions about the figure 1.1 • >>> table = [[node, rounded_degree_centrality[node], rounded_closeness_centrality[node], rounded_bet_centrality[node], rounded_eigenvector_centrality[node]] for node in g.nodes()] LINK@KOREATECH

  7. Putting It Together • Build a table with four centralities LINK@KOREATECH

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