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Python NetworkX-属性オプションの数に基づいてノードの色を自動的に設定します

NetworkXを使用してソーシャルネットワークを分析および視覚化しています。多くの場合、ネットワーク内のノードには、部門などの属性情報が関連付けられています。ただし、属性の分割には、オプションがいくつあるかは常にわかりません。たとえば、ネットワーク内にノード属性として表される部門が3つしかない場合や、30の部門がある場合があります。

ノード属性に基づいてノードの色を設定する方法を理解しました（以下のコードを参照）。ただし、この例では、ノード属性グループ（5つのオプション）にさまざまなオプションがあることを知っていて、各色を自動的に設定しました。

ノードアトリビュートのオプションが3つまたは5つしかない場合、ノードアトリビュートの色を選択することは難しくありませんが、さらに多くのオプションがある場合、これは非現実的になります。

私が理解したいのは、提供されたノード属性オプションの数に基づいて、最適なノード属性の色を自動的に選択する方法です。

色付けする属性に5つのオプションがある場合と、色付けするノード属性に30のオプションがある場合があり、各ノードの色を個別に設定したくありません。

これがcolormap関数を使用して実行できることなのか、それとも、中心度などの数値測定によるカラーノードの場合だけなのかどうかはわかりません。

NETWORKXコード

import networkx as nx

pylab inline

# create an empty graph
g = nx.Graph()

# open csv edgelist and read edges into graph
for line in open('phils_network_edgelist.csv', 'rb'):
    Edge = line.rstrip().split(',')
    g.add_Edge(edge[0], Edge[1])

# draw network without node color
nx.draw(g, with_labels=False, node_size=25)

network without node attribute color

# read in node attributes as list of tuples
group_attr = []
for line in open('phils_network_attribute_group.csv', 'rb'):
    group_attr.append(Tuple(line.rstrip().split(',')))

# convert list of tuples into a dict
group_attr_dict = dict(set(sorted(group_attr)))

# set nodes attributes
nx.set_node_attributes(g, "group", group_attr_dict)

# create empty list for node colors
node_color = []

# for each node in the graph
for node in g.nodes(data=True):

    # if the node has the attribute group1
    if 'group1' in node[1]['group']:
        node_color.append('blue')

    # if the node has the attribute group1
    Elif 'group2' in node[1]['group']:
        node_color.append('red')

    # if the node has the attribute group1
    Elif 'group3' in node[1]['group']:
        node_color.append('green')

    # if the node has the attribute group1
    Elif 'group4' in node[1]['group']:
        node_color.append('yellow')

    # if the node has the attribute group1
    Elif 'group5' in node[1]['group']:
        node_color.append('orange')  

# draw graph with node attribute color
nx.draw(g, with_labels=False, node_size=25, node_color=node_color)

network with node attribute color

ネットワークデータ

In[58]: 

g.nodes(data=True)

Out[58]:

[('BD', {'group': 'group5'}),
 ('WC', {'group': 'group3'}),
 ('BA', {'group': 'group4'}),
 ('WM', {'group': 'group3'}),
 ('JR', {'group': 'group1'}),
 ('JS', {'group': 'group3'}),
 ('JL', {'group': 'group4'}),
 ('JM', {'group': 'group2'}),
 ('JK', {'group': 'group2'}),
 ('JF', {'group': 'group2'}),
 ('JG', {'group': 'group2'}),
 ('JA', {'group': 'group2'}),
 ('JB', {'group': 'group4'}),
 ('JC', {'group': 'group4'}),
 ('RR', {'group': 'group3'}),
 ('RS', {'group': 'group3'}),
 ('TTI', {'group': 'group3'}),
 ('RB', {'group': 'group1'}),
 ('RL', {'group': 'group3'}),
 ('RO', {'group': 'group4'}),
 ('LHA', {'group': 'group2'}),
 ('LHI', {'group': 'group1'}),
 ('GF', {'group': 'group2'}),
 ('GB', {'group': 'group4'}),
 ('EM', {'group': 'group2'}),
 ('HR', {'group': 'group5'}),
 ('BS', {'group': 'group3'}),
 ('HH', {'group': 'group4'}),
 ('HA', {'group': 'group1'}),
 ('PS', {'group': 'group1'}),
 ('PW', {'group': 'group1'}),
 ('PB', {'group': 'group1'}),
 ('PC', {'group': 'group5'}),
 ('MFR', {'group': 'group4'}),
 ('JMA', {'group': 'group5'}),
 ('PN', {'group': 'group4'}),
 ('PL', {'group': 'group3'}),
 ('ZL', {'group': 'group4'}),
 ('EB', {'group': 'group2'}),
 ('ET', {'group': 'group3'}),
 ('EW', {'group': 'group1'}),
 ('ER', {'group': 'group3'}),
 ('MF', {'group': 'group3'}),
 ('MA', {'group': 'group4'}),
 ('MM', {'group': 'group2'}),
 ('MN', {'group': 'group4'}),
 ('MH', {'group': 'group3'}),
 ('MK', {'group': 'group2'}),
 ('JLA', {'group': 'group2'}),
 ('MP', {'group': 'group1'}),
 ('MS', {'group': 'group4'}),
 ('MR', {'group': 'group4'}),
 ('FI', {'group': 'group5'}),
 ('CJ', {'group': 'group4'}),
 ('CO', {'group': 'group5'}),
 ('CM', {'group': 'group4'}),
 ('CB', {'group': 'group2'}),
 ('CG', {'group': 'group2'}),
 ('CF', {'group': 'group5'}),
 ('CD', {'group': 'group3'}),
 ('CS', {'group': 'group2'}),
 ('CP', {'group': 'group2'}),
 ('CV', {'group': 'group2'}),
 ('KC', {'group': 'group1'}),
 ('KB', {'group': 'group3'}),
 ('SY', {'group': 'group2'}),
 ('KF', {'group': 'group2'}),
 ('KD', {'group': 'group3'}),
 ('KH', {'group': 'group1'}),
 ('SW', {'group': 'group1'}),
 ('KL', {'group': 'group2'}),
 ('KP', {'group': 'group3'}),
 ('KW', {'group': 'group1'}),
 ('SM', {'group': 'group2'}),
 ('SB', {'group': 'group4'}),
 ('DJ', {'group': 'group2'}),
 ('DD', {'group': 'group2'}),
 ('DV', {'group': 'group5'}),
 ('BJ', {'group': 'group3'}),
 ('DR', {'group': 'group2'}),
 ('KWI', {'group': 'group4'}),
 ('TW', {'group': 'group2'}),
 ('TT', {'group': 'group2'}),
 ('LH', {'group': 'group3'}),
 ('LW', {'group': 'group3'}),
 ('TM', {'group': 'group3'}),
 ('LS', {'group': 'group3'}),
 ('LP', {'group': 'group2'}),
 ('TG', {'group': 'group3'}),
 ('JCU', {'group': 'group2'}),
 ('AL', {'group': 'group1'}),
 ('AP', {'group': 'group3'}),
 ('AS', {'group': 'group3'}),
 ('IM', {'group': 'group4'}),
 ('AW', {'group': 'group3'}),
 ('HHI', {'group': 'group1'})]

In [59]:

g.edges(data=True)

Out[59]:

[('BD', 'ZL', {}),
 ('BD', 'JCU', {}),
 ('BD', 'DJ', {}),
 ('BD', 'BA', {}),
 ('BD', 'CB', {}),
 ('BD', 'CG', {}),
 ('BD', 'AS', {}),
 ('BD', 'MH', {}),
 ('BD', 'AP', {}),
 ('BD', 'HH', {}),
 ('BD', 'TM', {}),
 ('BD', 'CF', {}),
 ('BD', 'CP', {}),
 ('BD', 'DR', {}),
 ('BD', 'CV', {}),
 ('BD', 'EB', {}),
 ('WC', 'JCU', {}),
 ('WC', 'JS', {}),
 ('BA', 'JR', {}),
 ('BA', 'JB', {}),
 ('BA', 'RR', {}),
 ('BA', 'RS', {}),
 ('BA', 'LH', {}),
 ('BA', 'PC', {}),
 ('BA', 'TTI', {}),
 ('BA', 'PL', {}),
 ('BA', 'JCU', {}),
 ('BA', 'CF', {}),
 ('BA', 'EB', {}),
 ('BA', 'GF', {}),
 ('BA', 'AS', {}),
 ('BA', 'IM', {}),
 ('BA', 'BJ', {}),
 ('BA', 'CS', {}),
 ('BA', 'KH', {}),
 ('BA', 'SW', {}),
 ('BA', 'MH', {}),
 ('BA', 'MR', {}),
 ('BA', 'HHI', {}),
 ('WM', 'EM', {}),
 ('WM', 'JCU', {}),
 ('WM', 'CO', {}),
 ('WM', 'LP', {}),
 ('WM', 'AW', {}),
 ('WM', 'KD', {}),
 ('WM', 'TT', {}),
 ('WM', 'JS', {}),
 ('WM', 'PB', {}),
 ('WM', 'JM', {}),
 ('WM', 'MFR', {}),
 ('WM', 'RB', {}),
 ('WM', 'MR', {}),
 ('WM', 'DV', {}),
 ('WM', 'TG', {}),
 ('WM', 'JF', {}),
 ('WM', 'JMA', {}),
 ('WM', 'FI', {}),
 ('WM', 'JB', {}),
 ('JR', 'GF', {}),
 ('JR', 'MFR', {}),
 ('JR', 'KH', {}),
 ('JR', 'JB', {}),
 ('JS', 'EM', {}),
 ('JS', 'PS', {}),
 ('JS', 'MF', {}),
 ('JS', 'JCU', {}),
 ('JS', 'KD', {}),
 ('JS', 'MH', {}),
 ('JS', 'TTI', {}),
 ('JS', 'RB', {}),
 ('JS', 'TG', {}),
 ('JL', 'KB', {}),
 ('JL', 'MN', {}),
 ('JL', 'LW', {}),
 ('JL', 'CS', {}),
 ('JL', 'ET', {}),
 ('JL', 'ER', {}),
 ('JM', 'EM', {}),
 ('JM', 'PS', {}),
 ('JM', 'KD', {}),
 ('JM', 'CD', {}),
 ('JM', 'JK', {}),
 ('JM', 'TG', {}),
 ('JM', 'RO', {}),
 ('JM', 'CV', {}),
 ('JK', 'HR', {}),
 ('JK', 'PS', {}),
 ('JF', 'EM', {}),
 ('JF', 'PS', {}),
 ('JF', 'LP', {}),
 ('JF', 'LHA', {}),
 ('JF', 'CD', {}),
 ('JF', 'RB', {}),
 ('JF', 'JG', {}),
 ('JF', 'KF', {}),
 ('JG', 'CJ', {}),
 ('JG', 'SY', {}),
 ('JG', 'KF', {}),
 ('JG', 'LHA', {}),
 ('JG', 'CD', {}),
 ('JG', 'RB', {}),
 ('JG', 'BS', {}),
 ('JA', 'CS', {}),
 ('JB', 'KC', {}),
 ('JB', 'JCU', {}),
 ('JB', 'MA', {}),
 ('JB', 'AW', {}),
 ('JB', 'KWI', {}),
 ('JB', 'KH', {}),
 ('JB', 'CF', {}),
 ('JB', 'EB', {}),
 ('JB', 'PB', {}),
 ('JB', 'MFR', {}),
 ('JB', 'KW', {}),
 ('JB', 'RB', {}),
 ('JB', 'MR', {}),
 ('JB', 'RL', {}),
 ('JB', 'FI', {}),
 ('JB', 'JMA', {}),
 ('JC', 'SM', {}),
 ('RR', 'MS', {}),
 ('RR', 'SW', {}),
 ('RR', 'LH', {}),
 ('RS', 'LH', {}),
 ('TTI', 'JCU', {}),
 ('TTI', 'SW', {}),
 ('TTI', 'CF', {}),
 ('RB', 'EM', {}),
 ('RB', 'PS', {}),
 ('RB', 'SY', {}),
 ('RB', 'JCU', {}),
 ('RB', 'KD', {}),
 ('RB', 'CF', {}),
 ('RB', 'LHI', {}),
 ('RB', 'CD', {}),
 ('RB', 'MH', {}),
 ('RB', 'CJ', {}),
 ('RB', 'TG', {}),
 ('RB', 'EB', {}),
 ('RO', 'PS', {}),
 ('LHA', 'CJ', {}),
 ('LHA', 'SY', {}),
 ('LHA', 'KF', {}),
 ('LHA', 'CD', {}),
 ('LHI', 'PS', {}),
 ('LHI', 'CJ', {}),
 ('GF', 'KC', {}),
 ('GF', 'MA', {}),
 ('GB', 'HR', {}),
 ('GB', 'MM', {}),
 ('GB', 'LS', {}),
 ('EM', 'LP', {}),
 ('EM', 'DV', {}),
 ('EM', 'TG', {}),
 ('HR', 'MM', {}),
 ('HR', 'MH', {}),
 ('HR', 'EB', {}),
 ('HR', 'LS', {}),
 ('BS', 'CD', {}),
 ('HH', 'ZL', {}),
 ('HH', 'CB', {}),
 ('HH', 'CP', {}),
 ('HH', 'DR', {}),
 ('HH', 'CV', {}),
 ('HA', 'SM', {}),
 ('PS', 'KD', {}),
 ('PS', 'CF', {}),
 ('PS', 'TG', {}),
 ('PW', 'CM', {}),
 ('PW', 'TW', {}),
 ('PW', 'TT', {}),
 ('PW', 'MH', {}),
 ('PW', 'AL', {}),
 ('PW', 'MP', {}),
 ('PW', 'CS', {}),
 ('PW', 'HHI', {}),
 ('PW', 'EW', {}),
 ('PB', 'CO', {}),
 ('PB', 'KH', {}),
 ('PB', 'CF', {}),
 ('PB', 'MFR', {}),
 ('PB', 'AW', {}),
 ('PB', 'MA', {}),
 ('PC', 'CS', {}),
 ('PC', 'JCU', {}),
 ('PC', 'SW', {}),
 ('MFR', 'KC', {}),
 ('MFR', 'JCU', {}),
 ('MFR', 'KH', {}),
 ('MFR', 'MH', {}),
 ('MFR', 'MR', {}),
 ('JMA', 'KWI', {}),
 ('JMA', 'AW', {}),
 ('PN', 'SB', {}),
 ('PL', 'HHI', {}),
 ('PL', 'MK', {}),
 ('PL', 'LH', {}),
 ('ZL', 'CB', {}),
 ('ZL', 'AP', {}),
 ('ZL', 'CP', {}),
 ('ZL', 'DR', {}),
 ('ZL', 'CV', {}),
 ('EB', 'JCU', {}),
 ('EB', 'DJ', {}),
 ('EB', 'CM', {}),
 ('EB', 'SW', {}),
 ('EB', 'MM', {}),
 ('EB', 'LS', {}),
 ('EB', 'CS', {}),
 ('EB', 'CP', {}),
 ('EB', 'CV', {}),
 ('ET', 'LW', {}),
 ('ET', 'ER', {}),
 ('ET', 'KB', {}),
 ('EW', 'TW', {}),
 ('EW', 'TT', {}),
 ('EW', 'HHI', {}),
 ('EW', 'AL', {}),
 ('ER', 'LW', {}),
 ('ER', 'KB', {}),
 ('MA', 'KW', {}),
 ('MA', 'AW', {}),
 ('MA', 'MR', {}),
 ('MM', 'LS', {}),
 ('MH', 'JCU', {}),
 ('MH', 'SY', {}),
 ('MH', 'DJ', {}),
 ('MH', 'CM', {}),
 ('MH', 'AL', {}),
 ('MH', 'SW', {}),
 ('MH', 'CF', {}),
 ('MH', 'LS', {}),
 ('MH', 'CS', {}),
 ('MH', 'TG', {}),
 ('MH', 'CP', {}),
 ('MH', 'CV', {}),
 ('MK', 'LH', {}),
 ('MK', 'KL', {}),
 ('MK', 'JLA', {}),
 ('MK', 'MS', {}),
 ('MK', 'CS', {}),
 ('JLA', 'CM', {}),
 ('JLA', 'KL', {}),
 ('JLA', 'MS', {}),
 ('JLA', 'CS', {}),
 ('JLA', 'SB', {}),
 ('JLA', 'HHI', {}),
 ('MP', 'TW', {}),
 ('MP', 'TT', {}),
 ('MP', 'HHI', {}),
 ('MS', 'CS', {}),
 ('MS', 'HHI', {}),
 ('FI', 'KW', {}),
 ('FI', 'AW', {}),
 ('FI', 'CF', {}),
 ('CJ', 'SY', {}),
 ('CJ', 'DD', {}),
 ('CJ', 'CD', {}),
 ('CO', 'AW', {}),
 ('CM', 'TW', {}),
 ('CM', 'TT', {}),
 ('CM', 'AL', {}),
 ('CM', 'CS', {}),
 ('CB', 'DJ', {}),
 ('CB', 'CP', {}),
 ('CB', 'CV', {}),
 ('CG', 'CF', {}),
 ('CF', 'JCU', {}),
 ('CF', 'AW', {}),
 ('CF', 'KH', {}),
 ('CF', 'LH', {}),
 ('CF', 'AP', {}),
 ('CF', 'AS', {}),
 ('CF', 'KW', {}),
 ('CF', 'CS', {}),
 ('CF', 'CV', {}),
 ('CD', 'SY', {}),
 ('CD', 'LP', {}),
 ('CD', 'KF', {}),
 ('CS', 'JCU', {}),
 ('CS', 'TW', {}),
 ('CS', 'TT', {}),
 ('CS', 'AS', {}),
 ('CS', 'LH', {}),
 ('CS', 'SB', {}),
 ('CS', 'HHI', {}),
 ('CP', 'DJ', {}),
 ('CP', 'AP', {}),
 ('CP', 'DR', {}),
 ('CP', 'CV', {}),
 ('CV', 'DJ', {}),
 ('CV', 'AP', {}),
 ('CV', 'DR', {}),
 ('KB', 'LW', {}),
 ('SY', 'KF', {}),
 ('KF', 'AP', {}),
 ('KD', 'TG', {}),
 ('SW', 'BJ', {}),
 ('SW', 'IM', {}),
 ('SW', 'LH', {}),
 ('KL', 'TT', {}),
 ('KP', 'TM', {}),
 ('KW', 'JCU', {}),
 ('SB', 'AL', {}),
 ('DJ', 'TG', {}),
 ('BJ', 'IM', {}),
 ('KWI', 'AW', {}),
 ('TW', 'TT', {}),
 ('TW', 'AL', {}),
 ('TW', 'HHI', {}),
 ('TT', 'AL', {}),
 ('TT', 'HHI', {}),
 ('LH', 'JCU', {}),
 ('JCU', 'AP', {}),
 ('JCU', 'AS', {}),
 ('AL', 'HHI', {})]
pythonmatplotlibnetworkx
18
CurtLH

以下は、カラーマップの使用例です。それは少しトリッキーです。カスタマイズされた離散カラーマップが必要な場合は、これを試すことができますSO答え Matplotlib離散カラーバー

import matplotlib.pyplot as plt
# create number for each group to allow use of colormap
from itertools import count
# get unique groups
groups = set(nx.get_node_attributes(g,'group').values())
mapping = dict(Zip(sorted(groups),count()))
nodes = g.nodes()
colors = [mapping[g.node[n]['group']] for n in nodes]

# drawing nodes and edges separately so we can capture collection for colobar
pos = nx.spring_layout(g)
ec = nx.draw_networkx_edges(g, pos, alpha=0.2)
nc = nx.draw_networkx_nodes(g, pos, nodelist=nodes, node_color=colors, 
                            with_labels=False, node_size=100, cmap=plt.cm.jet)
plt.colorbar(nc)
plt.axis('off')
plt.show()

enter image description here

25
Aric