Scikit-network-02：载图

载图

在Scikit网络中，图形由其scipy的压缩稀疏行格式中的邻接矩阵（或二部图矩阵）表示。在本教程中，我们提供了一些方法来实例化此格式的图。

from IPython.display import SVG

import numpy as np
from scipy import sparse
import pandas as pd

from sknetwork.data import from_edge_list, from_adjacency_list, from_graphml, from_csv
from sknetwork.visualization import svg_graph, svg_bigraph

NumPy array

对于小图，可以将邻接矩阵作为密集的numpy阵列实例化，并将其转换为CSR格式的稀疏矩阵。

adjacency = np.array([[0, 1, 1, 0], [1, 0, 1, 1], [1, 1, 0, 0], [0, 1, 0, 0]])
adjacency = sparse.csr_matrix(adjacency)

image = svg_graph(adjacency)
SVG(image)

在这里插入图片描述

Edge list

构建图形的另一种自然方法是来自边集列表。

edge_list = [(0, 1), (1, 2), (2, 3), (3, 0), (0, 2)]
adjacency = from_edge_list(edge_list)

image = svg_graph(adjacency)
SVG(image)

在这里插入图片描述

默认情况下，该图是无方向性的，但是可以轻松地添加参数directed=True将其变成定向化。

adjacency = from_edge_list(edge_list, directed=True)

image = svg_graph(adjacency)
SVG(image)

在这里插入图片描述

能还想在边缘增加重量。只需使用三元组而不是二元组：

edge_list = [(0, 1, 1), (1, 2, 0.5), (2, 3, 1), (3, 0, 0.5), (0, 2, 2)]
adjacency = from_edge_list(edge_list)

image = svg_graph(adjacency)
SVG(image)

在这里插入图片描述

也可以实例化两部图

edge_list = [(0, 0), (1, 0), (1, 1), (2, 1)]
biadjacency = from_edge_list(edge_list, bipartite=True)

image = svg_bigraph(biadjacency)
SVG(image)

在这里插入图片描述

如果未索引节点，则将获得带有图形属性（节点名称）的类型对象。

edge_list = [("Alice", "Bob"), ("Bob", "Carey"), ("Alice", "David"), ("Carey", "David"), ("Bob", "David")]
graph = from_edge_list(edge_list)

graph

{'names': array(['Alice', 'Bob', 'Carey', 'David', 'alice', 'bob'], dtype='<U5'),
 'adjacency': <6x6 sparse matrix of type '<class 'numpy.int64'>'
 	with 10 stored elements in Compressed Sparse Row format>}

adjacency = graph.adjacency

image = svg_graph(adjacency, names=names)

SVG(image)

在这里插入图片描述

默认情况下，每个边的权重是相应链接的出现次数：

edge_list_new = edge_list + [("Alice", "Bob"), ("Alice", "David"), ("Alice", "Bob")]
graph = from_edge_list(edge_list_new)

adjacency = graph.adjacency
names = graph.names

image = svg_graph(adjacency, names=names)
SVG(image)

在这里插入图片描述

也可以使得图变成无权图

graph = from_edge_list(edge_list_new, weighted=False)
adjacency = graph.adjacency
names = graph.names

image = svg_graph(adjacency, names=names)
SVG(image)

在这里插入图片描述

同样，可以将图变得有向：

graph = from_edge_list(edge_list, directed=True)

adjacency = graph.adjacency
names = graph.names

image = svg_graph(adjacency, names=names)
SVG(image)

在这里插入图片描述

图还可以具有明确的权重：

edge_list = [("Alice", "Bob", 3), ("Bob", "Carey", 2), ("Alice", "David", 1), ("Carey", "David", 2), ("Bob", "David", 3)]

graph = from_edge_list(edge_list)

adjacency = graph.adjacency
names = graph.names

image = svg_graph(
    adjacency, names=names, display_edge_weight=True, display_node_weight=True)

SVG(image)

在这里插入图片描述

对于二部图，

edge_list = [("Alice", "Football"), ("Bob", "Tennis"), ("David", "Football"), ("Carey", "Tennis"), ("Carey", "Football")]

graph = from_edge_list(edge_list, bipartite=True)

biadjacency = graph.biadjacency
names = graph.names
names_col = graph.names_col

image = svg_bigraph(biadjacency, names_row=names, names_col=names_col)
SVG(image)

在这里插入图片描述

Adjacency list

还可以从邻接列表中加载图形，作为列表列表或列表词典给出的图形：

adjacency_list =[[0, 1, 2], [2, 3]]
adjacency = from_adjacency_list(adjacency_list, directed=True)

image = svg_graph(adjacency)
SVG(image)

在这里插入图片描述

adjacency_dict = {"Alice": ["Bob", "David"], "Bob": ["Carey", "David"]}
graph = from_adjacency_list(adjacency_dict, directed=True)

adjacency = graph.adjacency
names = graph.names

image = svg_graph(adjacency, names=names)
SVG(image)

在这里插入图片描述

Dataframe

Dataframe可能包括边集列表。

miserables.tsv

Myriel	Napoleon
Myriel	Mlle Baptistine
Myriel	Mme Magloire
Myriel	Countess de Lo
Myriel	Geborand
Myriel	Champtercier
Myriel	Cravatte
Myriel	Count
Myriel	Old man
Myriel	Valjean
Mlle Baptistine	Mme Magloire
Mlle Baptistine	Valjean
Mme Magloire	Valjean
Labarre	Valjean
Valjean	Marguerite
Valjean	Mme Der
Valjean	Isabeau
Valjean	Gervais
Valjean	Fantine
Valjean	Mme Thenardier
Valjean	Thenardier
Valjean	Cosette
Valjean	Javert
Valjean	Fauchelevent
Valjean	Bamatabois
Valjean	Simplice
Valjean	Scaufflaire
Valjean	Woman1
Valjean	Judge
Valjean	Champmathieu
Valjean	Brevet
Valjean	Chenildieu
Valjean	Cochepaille
Valjean	Woman2
Valjean	MotherInnocent
Valjean	Gavroche
Valjean	Gillenormand
Valjean	Mlle Gillenormand
Valjean	Marius
Valjean	Enjolras
Valjean	Bossuet
Valjean	Gueulemer
Valjean	Babet
Valjean	Claquesous
Valjean	Montparnasse
Valjean	Toussaint
Marguerite	Fantine
Tholomyes	Listolier
Tholomyes	Fameuil
Tholomyes	Blacheville
Tholomyes	Favourite
Tholomyes	Dahlia
Tholomyes	Zephine
Tholomyes	Fantine
Tholomyes	Cosette
Tholomyes	Marius
Listolier	Fameuil
Listolier	Blacheville
Listolier	Favourite
Listolier	Dahlia
Listolier	Zephine
Listolier	Fantine
Fameuil	Blacheville
Fameuil	Favourite
Fameuil	Dahlia
Fameuil	Zephine
Fameuil	Fantine
Blacheville	Favourite
Blacheville	Dahlia
Blacheville	Zephine
Blacheville	Fantine
Favourite	Dahlia
Favourite	Zephine
Favourite	Fantine
Dahlia	Zephine
Dahlia	Fantine
Zephine	Fantine
Fantine	Mme Thenardier
Fantine	Thenardier
Fantine	Javert
Fantine	Bamatabois
Fantine	Perpetue
Fantine	Simplice
Mme Thenardier	Thenardier
Mme Thenardier	Cosette
Mme Thenardier	Javert
Mme Thenardier	Eponine
Mme Thenardier	Anzelma
Mme Thenardier	Magnon
Mme Thenardier	Gueulemer
Mme Thenardier	Babet
Mme Thenardier	Claquesous
Thenardier	Cosette
Thenardier	Javert
Thenardier	Pontmercy
Thenardier	Boulatruelle
Thenardier	Eponine
Thenardier	Anzelma
Thenardier	Gavroche
Thenardier	Marius
Thenardier	Gueulemer
Thenardier	Babet
Thenardier	Claquesous
Thenardier	Montparnasse
Thenardier	Brujon
Cosette	Javert
Cosette	Woman2
Cosette	Gillenormand
Cosette	Mlle Gillenormand
Cosette	Lt Gillenormand
Cosette	Marius
Cosette	Toussaint
Javert	Fauchelevent
Javert	Bamatabois
Javert	Simplice
Javert	Woman1
Javert	Woman2
Javert	Gavroche
Javert	Enjolras
Javert	Gueulemer
Javert	Babet
Javert	Claquesous
Javert	Montparnasse
Javert	Toussaint
Fauchelevent	MotherInnocent
Fauchelevent	Gribier
Bamatabois	Judge
Bamatabois	Champmathieu
Bamatabois	Brevet
Bamatabois	Chenildieu
Bamatabois	Cochepaille
Perpetue	Simplice
Judge	Champmathieu
Judge	Brevet
Judge	Chenildieu
Judge	Cochepaille
Champmathieu	Brevet
Champmathieu	Chenildieu
Champmathieu	Cochepaille
Brevet	Chenildieu
Brevet	Cochepaille
Chenildieu	Cochepaille
Pontmercy	Mme Pontmercy
Pontmercy	Marius
Eponine	Anzelma
Eponine	Marius
Eponine	Mabeuf
Eponine	Courfeyrac
Eponine	Gueulemer
Eponine	Babet
Eponine	Claquesous
Eponine	Montparnasse
Eponine	Brujon
Jondrette	Mme Burgon
Mme Burgon	Gavroche
Gavroche	Marius
Gavroche	Mabeuf
Gavroche	Enjolras
Gavroche	Combeferre
Gavroche	Prouvaire
Gavroche	Feuilly
Gavroche	Courfeyrac
Gavroche	Bahorel
Gavroche	Bossuet
Gavroche	Joly
Gavroche	Grantaire
Gavroche	Gueulemer
Gavroche	Babet
Gavroche	Montparnasse
Gavroche	Child1
Gavroche	Child2
Gavroche	Brujon
Gavroche	Mme Hucheloup
Gillenormand	Magnon
Gillenormand	Mlle Gillenormand
Gillenormand	Lt Gillenormand
Gillenormand	Marius
Gillenormand	Baroness
Mlle Gillenormand	Mme Pontmercy
Mlle Gillenormand	Mlle Vaubois
Mlle Gillenormand	Lt Gillenormand
Mlle Gillenormand	Marius
Lt Gillenormand	Marius
Marius	Baroness
Marius	Mabeuf
Marius	Enjolras
Marius	Combeferre
Marius	Feuilly
Marius	Courfeyrac
Marius	Bahorel
Marius	Bossuet
Marius	Joly
Mabeuf	Enjolras
Mabeuf	Combeferre
Mabeuf	Feuilly
Mabeuf	Courfeyrac
Mabeuf	Bahorel
Mabeuf	Bossuet
Mabeuf	Joly
Mabeuf	MotherPlutarch
Enjolras	Combeferre
Enjolras	Prouvaire
Enjolras	Feuilly
Enjolras	Courfeyrac
Enjolras	Bahorel
Enjolras	Bossuet
Enjolras	Joly
Enjolras	Grantaire
Enjolras	Claquesous
Enjolras	Mme Hucheloup
Combeferre	Prouvaire
Combeferre	Feuilly
Combeferre	Courfeyrac
Combeferre	Bahorel
Combeferre	Bossuet
Combeferre	Joly
Combeferre	Grantaire
Prouvaire	Feuilly
Prouvaire	Courfeyrac
Prouvaire	Bahorel
Prouvaire	Bossuet
Prouvaire	Joly
Prouvaire	Grantaire
Feuilly	Courfeyrac
Feuilly	Bahorel
Feuilly	Bossuet
Feuilly	Joly
Feuilly	Grantaire
Courfeyrac	Bahorel
Courfeyrac	Bossuet
Courfeyrac	Joly
Courfeyrac	Grantaire
Courfeyrac	Mme Hucheloup
Bahorel	Bossuet
Bahorel	Joly
Bahorel	Grantaire
Bahorel	Mme Hucheloup
Bossuet	Joly
Bossuet	Grantaire
Bossuet	Mme Hucheloup
Joly	Grantaire
Joly	Mme Hucheloup
Grantaire	Mme Hucheloup
Gueulemer	Babet
Gueulemer	Claquesous
Gueulemer	Montparnasse
Gueulemer	Brujon
Babet	Claquesous
Babet	Montparnasse
Babet	Brujon
Claquesous	Montparnasse
Claquesous	Brujon
Montparnasse	Brujon
Child1	Child2

df = pd.read_csv('miserables.tsv', sep='\t', names=['character_1', 'character_2'])

df.head()

edge_list = list(df.itertuples(index=False))
graph = from_edge_list(edge_list)
graph

adjacency = graph.adjacency
image = svg_graph(adjacency)
SVG(image)

	character_1	character_2
0	Myriel	Napoleon
1	Myriel	Mlle Baptistine
2	Myriel	Mme Magloire
3	Myriel	Countess de Lo
4	Myriel	Geborand


{'names': array(['Anzelma', 'Babet', 'Bahorel', 'Bamatabois', 'Baroness',
        'Blacheville', 'Bossuet', 'Boulatruelle', 'Brevet', 'Brujon',
        'Champmathieu', 'Champtercier', 'Chenildieu', 'Child1', 'Child2',
        'Claquesous', 'Cochepaille', 'Combeferre', 'Cosette', 'Count',
        'Countess de Lo', 'Courfeyrac', 'Cravatte', 'Dahlia', 'Enjolras',
        'Eponine', 'Fameuil', 'Fantine', 'Fauchelevent', 'Favourite',
        'Feuilly', 'Gavroche', 'Geborand', 'Gervais', 'Gillenormand',
        'Grantaire', 'Gribier', 'Gueulemer', 'Isabeau', 'Javert', 'Joly',
        'Jondrette', 'Judge', 'Labarre', 'Listolier', 'Lt Gillenormand',
        'Mabeuf', 'Magnon', 'Marguerite', 'Marius', 'Mlle Baptistine',
        'Mlle Gillenormand', 'Mlle Vaubois', 'Mme Burgon', 'Mme Der',
        'Mme Hucheloup', 'Mme Magloire', 'Mme Pontmercy', 'Mme Thenardier',
        'Montparnasse', 'MotherInnocent', 'MotherPlutarch', 'Myriel',
        'Napoleon', 'Old man', 'Perpetue', 'Pontmercy', 'Prouvaire',
        'Scaufflaire', 'Simplice', 'Thenardier', 'Tholomyes', 'Toussaint',
        'Valjean', 'Woman1', 'Woman2', 'Zephine'], dtype='<U17'),
 'adjacency': <77x77 sparse matrix of type '<class 'numpy.int64'>'
        with 508 stored elements in Compressed Sparse Row format>}

在这里插入图片描述

对于分类数据，可以使用Pandas在样本和特征之间获取两部分图。下面展示一个从成人收入数据集中获取的例子。

adult-income.csv

age,workclass,occupation,relationship,gender,income
40-49, State-gov, Adm-clerical, Not-in-family, Male, <=50K
50-59, Self-emp-not-inc, Exec-managerial, Husband, Male, <=50K
40-49, Private, Handlers-cleaners, Not-in-family, Male, <=50K
50-59, Private, Handlers-cleaners, Husband, Male, <=50K
30-39, Private, Prof-specialty, Wife, Female, <=50K
40-49, Private, Exec-managerial, Wife, Female, <=50K
50-59, Private, Other-service, Not-in-family, Female, <=50K
50-59, Self-emp-not-inc, Exec-managerial, Husband, Male, >50K
30-39, Private, Prof-specialty, Not-in-family, Female, >50K
40-49, Private, Exec-managerial, Husband, Male, >50K
40-49, Private, Exec-managerial, Husband, Male, >50K
30-39, State-gov, Prof-specialty, Husband, Male, >50K
20-29, Private, Adm-clerical, Own-child, Female, <=50K
30-39, Private, Sales, Not-in-family, Male, <=50K
40-49, Private, Craft-repair, Husband, Male, >50K
30-39, Private, Transport-moving, Husband, Male, <=50K
20-29, Self-emp-not-inc, Farming-fishing, Own-child, Male, <=50K
30-39, Private, Machine-op-inspct, Unmarried, Male, <=50K
40-49, Private, Sales, Husband, Male, <=50K
40-49, Self-emp-not-inc, Exec-managerial, Unmarried, Female, >50K
40-49, Private, Prof-specialty, Husband, Male, >50K
50-59, Private, Other-service, Unmarried, Female, <=50K
40-49, Federal-gov, Farming-fishing, Husband, Male, <=50K
40-49, Private, Transport-moving, Husband, Male, <=50K
60-69, Private, Tech-support, Unmarried, Female, <=50K
60-69, Local-gov, Tech-support, Husband, Male, >50K
20-29, Private, Craft-repair, Own-child, Male, <=50K
50-59, ?, ?, Husband, Male, >50K
40-49, Private, Exec-managerial, Not-in-family, Male, <=50K
50-59, Private, Craft-repair, Husband, Male, <=50K
20-29, Local-gov, Protective-serv, Not-in-family, Male, <=50K
20-29, Private, Sales, Own-child, Male, <=50K
40-49, Private, Exec-managerial, Own-child, Male, <=50K
30-39, Federal-gov, Adm-clerical, Own-child, Male, <=50K
20-29, State-gov, Other-service, Husband, Male, <=50K
50-59, Private, Machine-op-inspct, Unmarried, Male, <=50K
20-29, Private, Machine-op-inspct, Own-child, Male, <=50K
20-29, Private, Adm-clerical, Wife, Female, <=50K
30-39, Private, Sales, Husband, Male, >50K
50-59, Self-emp-not-inc, Prof-specialty, Husband, Male, <=50K
30-39, Private, Machine-op-inspct, Husband, Male, <=50K
50-59, Self-emp-not-inc, Prof-specialty, Husband, Male, <=50K
20-29, Private, Tech-support, Husband, Male, <=50K
50-59, Private, Adm-clerical, Unmarried, Female, <=50K
20-29, Private, Handlers-cleaners, Not-in-family, Male, <=50K
60-69, Federal-gov, Prof-specialty, Husband, Male, >50K
50-59, Private, Machine-op-inspct, Husband, Male, <=50K
40-49, Private, Exec-managerial, Unmarried, Female, <=50K
40-49, State-gov, Craft-repair, Husband, Male, <=50K

df = pd.read_csv('adult-income.csv')
df.head()

df_binary = pd.get_dummies(df, sparse=True)  # ont-hot encode
df_binary.head()

biadjacency = df_binary.sparse.to_coo()
# biadjacency matrix of the bipartite graph
biadjacency

age	workclass	occupation	relationship	gender	income
0	40-49	State-gov	Adm-clerical	Not-in-family	Male	<=50K
1	50-59	Self-emp-not-inc	Exec-managerial	Husband	Male	<=50K
2	40-49	Private	Handlers-cleaners	Not-in-family	Male	<=50K
3	50-59	Private	Handlers-cleaners	Husband	Male	<=50K
4	30-39	Private	Prof-specialty	Wife	Female	<=50K

	age_20-29	age_30-39	age_40-49	age_50-59	age_60-69	age_70-79	age_80-89	age_90-99	workclass_ ?	workclass_ Federal-gov	...	relationship_ Husband	relationship_ Not-in-family	relationship_ Other-relative	relationship_ Own-child	relationship_ Unmarried	relationship_ Wife	gender_ Female	gender_ Male	income_ <=50K	income_ >50K
0	0	0	1	0	0	0	0	0	0	0	...	0	1	0	0	0	0	0	1	1	0
1	0	0	0	1	0	0	0	0	0	0	...	1	0	0	0	0	0	0	1	1	0
2	0	0	1	0	0	0	0	0	0	0	...	0	1	0	0	0	0	0	1	1	0
3	0	0	0	1	0	0	0	0	0	0	...	1	0	0	0	0	0	0	1	1	0
4	0	1	0	0	0	0	0	0	0	0	...	0	0	0	0	0	1	1	0	1	0

<32561x42 sparse matrix of type '<class 'numpy.uint8'>'
	with 195366 stored elements in COOrdinate format>
	
# names of columns
names_col = list(df_binary)

biadjacency = sparse.csr_matrix(biadjacency)
image = svg_bigraph(biadjacency, names_col=names_col)
SVG(image)

CSV文件

可以直接从CSV或TSV文件加载图：

graph = from_csv('miserables.tsv')
graph

adjacency = graph.adjacency
image = svg_graph(adjacency)
SVG(image)

{'names': array(['Anzelma', 'Babet', 'Bahorel', 'Bamatabois', 'Baroness',
        'Blacheville', 'Bossuet', 'Boulatruelle', 'Brevet', 'Brujon',
        'Champmathieu', 'Champtercier', 'Chenildieu', 'Child1', 'Child2',
        'Claquesous', 'Cochepaille', 'Combeferre', 'Cosette', 'Count',
        'Countess de Lo', 'Courfeyrac', 'Cravatte', 'Dahlia', 'Enjolras',
        'Eponine', 'Fameuil', 'Fantine', 'Fauchelevent', 'Favourite',
        'Feuilly', 'Gavroche', 'Geborand', 'Gervais', 'Gillenormand',
        'Grantaire', 'Gribier', 'Gueulemer', 'Isabeau', 'Javert', 'Joly',
        'Jondrette', 'Judge', 'Labarre', 'Listolier', 'Lt Gillenormand',
        'Mabeuf', 'Magnon', 'Marguerite', 'Marius', 'Mlle Baptistine',
        'Mlle Gillenormand', 'Mlle Vaubois', 'Mme Burgon', 'Mme Der',
        'Mme Hucheloup', 'Mme Magloire', 'Mme Pontmercy', 'Mme Thenardier',
        'Montparnasse', 'MotherInnocent', 'MotherPlutarch', 'Myriel',
        'Napoleon', 'Old man', 'Perpetue', 'Pontmercy', 'Prouvaire',
        'Scaufflaire', 'Simplice', 'Thenardier', 'Tholomyes', 'Toussaint',
        'Valjean', 'Woman1', 'Woman2', 'Zephine'], dtype='<U17'),
 'adjacency': <77x77 sparse matrix of type '<class 'numpy.int64'>'
        with 508 stored elements in Compressed Sparse Row format>}

在这里插入图片描述

movie_actor.tsv

Inception	Leonardo DiCaprio
Inception	Marion Cotillard
Inception	Joseph Gordon Lewitt
The Dark Knight Rises	Marion Cotillard
The Dark Knight Rises	Joseph Gordon Lewitt
The Dark Knight Rises	Christian Bale
The Big Short	Christian Bale
The Big Short	Ryan Gosling
The Big Short	Brad Pitt
The Big Short	Steve Carell
Drive	Ryan Gosling
Drive	Carey Mulligan
The Great Gatsby	Leonardo DiCaprio
The Great Gatsby	Carey Mulligan
La La Land	Ryan Gosling
La La Land	Emma Stone
Crazy Stupid Love	Ryan Gosling
Crazy Stupid Love	Emma Stone
Crazy Stupid Love	Steve Carell
Vice	Christian Bale
Vice	Steve Carell
The Grand Budapest Hotel	Lea Seydoux
The Grand Budapest Hotel	Ralph Fiennes
The Grand Budapest Hotel	Jude Law
The Grand Budapest Hotel	Willem Dafoe
The Grand Budapest Hotel	Owen Wilson
Aviator	Leonardo DiCaprio
Aviator	Jude Law
Aviator	Willem Dafoe
007 Spectre	Lea Seydoux
007 Spectre	Ralph Fiennes
Inglourious Basterds	Brad Pitt
Inglourious Basterds	Lea Seydoux
Inglourious Basterds	Christophe Waltz
Midnight In Paris	Marion Cotillard
Midnight In Paris	Lea Seydoux
Midnight In Paris	Owen Wilson
Murder on the Orient Express	Willem Dafoe
Murder on the Orient Express	Johnny Depp
Fantastic Beasts 2	Jude Law
Fantastic Beasts 2	Johnny Depp

graph = from_csv('movie_actor.tsv', bipartite=True)
graph

biadjacency = graph.biadjacency
image = svg_bigraph(biadjacency)
SVG(image)

{'names_row': array(['007 Spectre', 'Aviator', 'Crazy Stupid Love', 'Drive',
        'Fantastic Beasts 2', 'Inception', 'Inglourious Basterds',
        'La La Land', 'Midnight In Paris', 'Murder on the Orient Express',
        'The Big Short', 'The Dark Knight Rises',
        'The Grand Budapest Hotel', 'The Great Gatsby', 'Vice'],
       dtype='<U28'),
 'names': array(['007 Spectre', 'Aviator', 'Crazy Stupid Love', 'Drive',
        'Fantastic Beasts 2', 'Inception', 'Inglourious Basterds',
        'La La Land', 'Midnight In Paris', 'Murder on the Orient Express',
        'The Big Short', 'The Dark Knight Rises',
        'The Grand Budapest Hotel', 'The Great Gatsby', 'Vice'],
       dtype='<U28'),
 'names_col': array(['Brad Pitt', 'Carey Mulligan', 'Christian Bale',
        'Christophe Waltz', 'Emma Stone', 'Johnny Depp',
        'Joseph Gordon Lewitt', 'Jude Law', 'Lea Seydoux',
        'Leonardo DiCaprio', 'Marion Cotillard', 'Owen Wilson',
        'Ralph Fiennes', 'Ryan Gosling', 'Steve Carell', 'Willem Dafoe'],
       dtype='<U28'),
 'biadjacency': <15x16 sparse matrix of type '<class 'numpy.int64'>'
 	with 41 stored elements in Compressed Sparse Row format>}

在这里插入图片描述

GraphML 文件

可以加载以GraphMl格式存储的图。格式参见https://en.wikipedia.org/wiki/GraphML

miserables.graphml

<?xml version='1.0' encoding='utf-8'?>
<graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd"><key id="d0" for="edge" attr.name="weight" attr.type="int"/>
<graph edgedefault="undirected"><node id="Anzelma"/>
<node id="Babet"/>
<node id="Bahorel"/>
<node id="Bamatabois"/>
<node id="Baroness"/>
<node id="Blacheville"/>
<node id="Bossuet"/>
<node id="Boulatruelle"/>
<node id="Brevet"/>
<node id="Brujon"/>
<node id="Champmathieu"/>
<node id="Champtercier"/>
<node id="Chenildieu"/>
<node id="Child1"/>
<node id="Child2"/>
<edge source="Anzelma" target="Eponine">
  <data key="d0">1</data>
</edge>
<edge source="Anzelma" target="Mme Thenardier">
  <data key="d0">1</data>
</edge>
<edge source="Anzelma" target="Thenardier">
  <data key="d0">1</data>
</edge>
<edge source="Babet" target="Brujon">
  <data key="d0">1</data>
</edge>
<edge source="Babet" target="Claquesous">
  <data key="d0">1</data>
</edge>
<edge source="Babet" target="Eponine">
  <data key="d0">1</data>
</edge>
<edge source="Babet" target="Gavroche">
  <data key="d0">1</data>
</edge>
<edge source="Babet" target="Gueulemer">
  <data key="d0">1</data>
</edge>
<edge source="Babet" target="Javert">
  <data key="d0">1</data>
</edge>
<edge source="Babet" target="Mme Thenardier">
  <data key="d0">1</data>
</edge>
<edge source="Babet" target="Montparnasse">
  <data key="d0">1</data>
</edge>
<edge source="Babet" target="Thenardier">
  <data key="d0">1</data>
</edge>
<edge source="Babet" target="Valjean">
  <data key="d0">1</data>
</edge>
<edge source="Bahorel" target="Bossuet">
  <data key="d0">1</data>
</edge>
<edge source="Bahorel" target="Combeferre">
  <data key="d0">1</data>
</edge>
<edge source="Bahorel" target="Courfeyrac">
  <data key="d0">1</data>
</edge>
<edge source="Bahorel" target="Enjolras">
  <data key="d0">1</data>
</edge>
<edge source="Bahorel" target="Feuilly">
  <data key="d0">1</data>
</edge>
<edge source="Bahorel" target="Gavroche">
  <data key="d0">1</data>
</edge>
<edge source="Bahorel" target="Grantaire">
  <data key="d0">1</data>
</edge>
<edge source="Bahorel" target="Joly">
  <data key="d0">1</data>
</edge>
<edge source="Bahorel" target="Mabeuf">
  <data key="d0">1</data>
</edge>
</graph></graphml>

graph = from_graphml('miserables.graphml')
adjacency = graph.adjacency
names = graph.names
names
image = svg_graph(adjacency)
SVG(image)

array(['Anzelma', 'Babet', 'Bahorel', 'Bamatabois', 'Baroness',
       'Blacheville', 'Bossuet', 'Boulatruelle', 'Brevet', 'Brujon',
       'Champmathieu', 'Champtercier', 'Chenildieu', 'Child1', 'Child2',
       'Claquesous', 'Cochepaille', 'Combeferre', 'Cosette', 'Count',
       'Countess de Lo', 'Courfeyrac', 'Cravatte', 'Dahlia', 'Enjolras',
       'Eponine', 'Fameuil', 'Fantine', 'Fauchelevent', 'Favourite',
       'Feuilly', 'Gavroche', 'Geborand', 'Gervais', 'Gillenormand',
       'Grantaire', 'Gribier', 'Gueulemer', 'Isabeau', 'Javert', 'Joly',
       'Jondrette', 'Judge', 'Labarre', 'Listolier', 'Lt Gillenormand',
       'Mabeuf', 'Magnon', 'Marguerite', 'Marius', 'Mlle Baptistine',
       'Mlle Gillenormand', 'Mlle Vaubois', 'Mme Burgon', 'Mme Der',
       'Mme Hucheloup', 'Mme Magloire', 'Mme Pontmercy', 'Mme Thenardier',
       'Montparnasse', 'MotherInnocent', 'MotherPlutarch', 'Myriel',
       'Napoleon', 'Old man', 'Perpetue', 'Pontmercy', 'Prouvaire',
       'Scaufflaire', 'Simplice', 'Thenardier', 'Tholomyes', 'Toussaint',
       'Valjean', 'Woman1', 'Woman2', 'Zephine'], dtype='<U512')

在这里插入图片描述

painters.graphml

<?xml version='1.0' encoding='utf-8'?>
<graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd"><key id="d0" for="edge" attr.name="weight" attr.type="int"/>
<graph edgedefault="directed"><node id="Claude Monet"/>
<node id="Edgar Degas"/>
<node id="Edouard Manet"/>
<node id="Egon Schiele"/>
<node id="Gustav Klimt"/>
<node id="Henri Matisse"/>
<node id="Leonardo da Vinci"/>
<node id="Michelangelo"/>
<node id="Pablo Picasso"/>
<node id="Paul Cezanne"/>
<node id="Peter Paul Rubens"/>
<node id="Pierre-Auguste Renoir"/>
<node id="Rembrandt"/>
<node id="Vincent van Gogh"/>
<edge source="Claude Monet" target="Edouard Manet">
  <data key="d0">1</data>
</edge>
<edge source="Claude Monet" target="Pierre-Auguste Renoir">
  <data key="d0">1</data>
</edge>
<edge source="Edgar Degas" target="Claude Monet">
  <data key="d0">1</data>
</edge>
</graph></graphml>

# Directed graph
graph = from_graphml('painters.graphml', directed=True)
adjacency = graph.adjacency
names = graph.names

image = svg_graph(adjacency, names=names)
SVG(image)

在这里插入图片描述

NetworkX

NetworkX具有从企业社会责任格式和朝向CSR格式的导入和导出功能。

其它选型

toy graphs
从模型生成图
从现有存储库中加载图表（参阅NetSet和Konect）

从现有存储库中加载图表

NetSet

graph = load_netset('openflights')
adjacency = graph.adjacency
names = graph.names

graph

image = svg_graph(adjacency)
SVG(image)

{'names': array(['Goroka Airport', 'Madang Airport', 'Mount Hagen Kagamuga Airport',
        ..., 'Saumlaki/Olilit Airport', 'Tarko-Sale Airport',
        'Alashankou Bole (Bortala) airport'], dtype='<U65'),
 'meta': {'name': 'openflights',
  'description': 'Airports with daily number of flights between them.',
  'source': 'https://openflights.org'},
 'position': array([[145.39199829,  -6.08168983],
        [145.78900147,  -5.20707989],
        [144.29600525,  -5.82678986],
        ...,
        [131.30599976,  -7.98860979],
        [ 77.81809998,  64.93080139],
        [ 82.3       ,  44.895     ]]),
 'adjacency': <3097x3097 sparse matrix of type '<class 'numpy.int64'>'
 	with 36386 stored elements in Compressed Sparse Row format>}

在这里插入图片描述

# Directed graph
graph = load_netset('wikivitals')
adjacency = graph.adjacency
names = graph.names
labels = graph.labels

image = svg_graph(adjacency, names=names, labels=labels)
SVG(image)

# Bipartite graph
graph = load_netset('cinema')
biadjacency = graph.biadjacency

Konect

graph = load_konect('dolphins')
adjacency = graph.adjacency

graph

image = svg_graph(adjacency)
SVG(image)