Python NetworkX girvan_newman用法及代碼示例

本文簡要介紹 networkx.algorithms.community.centrality.girvan_newman 的用法。

用法: girvan_newman(G, most_valuable_edge=None)

使用Girvan-Newman 方法在圖中查找社區。

參數：

G：NetworkX 圖

most_valuable_edge：函數

將圖形作為輸入並輸出邊的函數。此函數返回的邊將在算法的每次迭代中重新計算並刪除。

如果未指定，將使用具有最高networkx.edge_betweenness_centrality() 的邊。

迭代器: 迭代 G 中節點集的元組。每組節點是一個社區，每個元組是算法特定級別的社區序列。

注意：

Girvan-Newman 算法通過從原始圖中逐步刪除邊來檢測社區。該算法在每一步都刪除了“most valuable” 邊，傳統上是具有最高中介中心性的邊。隨著圖表分解成碎片，緊密結合的社區結構暴露出來，結果可以描繪為樹狀圖。

例子：

要獲得第一對社區：

>>> G = nx.path_graph(10)
>>> comp = girvan_newman(G)
>>> tuple(sorted(c) for c in next(comp))
([0, 1, 2, 3, 4], [5, 6, 7, 8, 9])

要僅獲取社區的前 k 元組，請使用 itertools.islice() ：

>>> import itertools
>>> G = nx.path_graph(8)
>>> k = 2
>>> comp = girvan_newman(G)
>>> for communities in itertools.islice(comp, k):
...     print(tuple(sorted(c) for c in communities))
...
([0, 1, 2, 3], [4, 5, 6, 7])
([0, 1], [2, 3], [4, 5, 6, 7])

要在社區數量大於 k 時停止獲取社區元組，請使用 itertools.takewhile() ：

>>> import itertools
>>> G = nx.path_graph(8)
>>> k = 4
>>> comp = girvan_newman(G)
>>> limited = itertools.takewhile(lambda c: len(c) <= k, comp)
>>> for communities in limited:
...     print(tuple(sorted(c) for c in communities))
...
([0, 1, 2, 3], [4, 5, 6, 7])
([0, 1], [2, 3], [4, 5, 6, 7])
([0, 1], [2, 3], [4, 5], [6, 7])

僅根據權重選擇要刪除的邊：

>>> from operator import itemgetter
>>> G = nx.path_graph(10)
>>> edges = G.edges()
>>> nx.set_edge_attributes(G, {(u, v): v for u, v in edges}, "weight")
>>> def heaviest(G):
...     u, v, w = max(G.edges(data="weight"), key=itemgetter(2))
...     return (u, v)
...
>>> comp = girvan_newman(G, most_valuable_edge=heaviest)
>>> tuple(sorted(c) for c in next(comp))
([0, 1, 2, 3, 4, 5, 6, 7, 8], [9])

在選擇具有例如最高中介中心性的邊時利用邊權重：

>>> from networkx import edge_betweenness_centrality as betweenness
>>> def most_central_edge(G):
...     centrality = betweenness(G, weight="weight")
...     return max(centrality, key=centrality.get)
...
>>> G = nx.path_graph(10)
>>> comp = girvan_newman(G, most_valuable_edge=most_central_edge)
>>> tuple(sorted(c) for c in next(comp))
([0, 1, 2, 3, 4], [5, 6, 7, 8, 9])

要為邊指定不同的排名算法，請使用 most_valuable_edge 關鍵字參數：

>>> from networkx import edge_betweenness_centrality
>>> from random import random
>>> def most_central_edge(G):
...     centrality = edge_betweenness_centrality(G)
...     max_cent = max(centrality.values())
...     # Scale the centrality values so they are between 0 and 1,
...     # and add some random noise.
...     centrality = {e: c / max_cent for e, c in centrality.items()}
...     # Add some random noise.
...     centrality = {e: c + random() for e, c in centrality.items()}
...     return max(centrality, key=centrality.get)
...
>>> G = nx.path_graph(10)
>>> comp = girvan_newman(G, most_valuable_edge=most_central_edge)

相關用法

注：本文由純淨天空篩選整理自networkx.org大神的英文原創作品 networkx.algorithms.community.centrality.girvan_newman。非經特殊聲明，原始代碼版權歸原作者所有，本譯文未經允許或授權，請勿轉載或複製。

用法:

參數：

返回：

注意：

例子：