Centrality Python Network X LINKKOREATECH 1 Degree Centrality
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Centrality – Python - Network. X LINK@KOREATECH 1
Degree Centrality – Find the “Celebrities” – Figure 1. 1 Network • >>> degree_centrality = net. degree_centrality(g) • >>> degree_centrality Ø {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} – Define sort_map funtion sort_map. py import operator def sort_map(map): sorted. List = map. items() sorted. List. sort(key=operator. itemgetter(1), reverse=True) return sorted. List – Sort the degree centrality • >>> import sort_map • >>> sorted_degree_centrality = sort_map(degree_centrality) • >>> sorted_degree_centrality Ø [(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)] LINK@KOREATECH 2
Closeness Centrality - Find the Gossipmongers – Figure 1. 1 Network • >>> closeness_centrality = net. closeness_centrality(g) • >>> closeness_centrality Ø {1: 0. 47058823529411764, 2: 0. 34782608695652173, 3: 0. 47058823529411764, 4: 0. 6153846154, 5: 0. 6153846154, 6: 0. 6153846154, 7: 0. 5, 8: 0. 47058823529411764, 9: 0. 34782608695652173} • >>> import sort_map • >>> sorted_closeness_centrality = sort_map(closeness_centrality) • >>> sorted_closeness_centrality Ø [(4, 0. 6153846154), (5, 0. 6153846154), (6, 0. 6153846154), (7, 0. 5), (1, 0. 47058823529411764), (3, 0. 47058823529411764), (8, 0. 47058823529411764), (2, 0. 34782608695652173), (9, 0. 34782608695652173)] LINK@KOREATECH 3
Betweenness Centrality - Find the Communication Bottlenecks and/or Community Bridges – Figure 1. 1 Network • >>> bet_centrality = net. betweenness_centrality(g) • >>> bet_centrality Ø {1: 0. 10714285714, 2: 0. 0, 3: 0. 10714285714, 4: 0. 5357142857, 5: 0. 21428571427, 6: 0. 21428571427, 7: 0. 25, 8: 0. 0, 9: 0. 0} • >>> import sort_map • >>> sorted_bet_centrality = sort_map(bet_centrality) • >>> sorted_bet_centrality Ø [(4, 0. 5357142857), (7, 0. 25), (5, 0. 21428571427), (6, 0. 21428571427), (1, 0. 10714285714), (3, 0. 10714285714), (2, 0. 0), (8, 0. 0), (9, 0. 0)] LINK@KOREATECH 4
Eigenvector Centrality – Figure 1. 1 Network • >>> eigenvector_centrality = net. eigenvector_centrality(g) • >>> eigenvector_centrality Ø {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(eigenvector_centrality) • >>> sorted_eigenvector_centrality Ø [(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 5
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 6
Putting It Together Build a table with four centralities node 1 2 3 4 5 6 7 8 9 degree 0. 375 0. 250 0. 375 0. 500 0. 375 0. 125 closeness 0. 471 0. 348 0. 471 0. 615 0. 500 0. 471 0. 348 betweenness 0. 107 0. 000 0. 107 0. 536 0. 214 0. 250 0. 000 LINK@KOREATECH eigenvector 0. 196 0. 112 0. 196 0. 379 0. 468 0. 410 0. 384 0. 117 7
