模型

此笔记本展示了如何基于简单模型加载一些图。

[1]:

from IPython.display import SVG

[2]:

import numpy as np

[3]:

from sknetwork.data import erdos_renyi, block_model, linear_graph, cyclic_graph, linear_digraph, cyclic_digraph, grid, albert_barabasi, watts_strogatz
from sknetwork.visualization import visualize_graph

Erdos-Renyi 模型

[4]:

adjacency = erdos_renyi(20, 0.2)

[5]:

image = visualize_graph(adjacency)
SVG(image)

[5]:

../../_images/tutorials_data_models_7_0.svg

随机块模型

[6]:

graph = block_model([20,25,30], p_in=[0.5,0.4,0.3], p_out=0.02, metadata=True)
adjacency = graph.adjacency
labels = graph.labels

[7]:

image = visualize_graph(adjacency, labels=labels)
SVG(image)

[7]:

../../_images/tutorials_data_models_10_0.svg

线性图

[8]:

graph = linear_graph(8, metadata=True)
adjacency = graph.adjacency
position = graph.position

[9]:

image = visualize_graph(adjacency, position)
SVG(image)

[9]:

../../_images/tutorials_data_models_13_0.svg

[10]:

# adjacency matrix only
adjacency = linear_graph(8)

[11]:

# directed
graph = linear_digraph(8, metadata=True)
adjacency = graph.adjacency
position = graph.position

[12]:

image = visualize_graph(adjacency, position)
SVG(image)

[12]:

../../_images/tutorials_data_models_16_0.svg

循环图

[13]:

graph = cyclic_graph(8, metadata=True)
adjacency = graph.adjacency
position = graph.position

[14]:

image = visualize_graph(adjacency, position, width=200, height=200)

[15]:

SVG(image)

[15]:

../../_images/tutorials_data_models_20_0.svg

网格

[16]:

graph = grid(6, 4, metadata=True)
adjacency = graph.adjacency
position = graph.position

[17]:

image = visualize_graph(adjacency, position)

[18]:

SVG(image)

[18]:

../../_images/tutorials_data_models_24_0.svg

Albert-Barabasi 模型

[19]:

adjacency = albert_barabasi(n=100, degree=3)

[20]:

image = visualize_graph(adjacency, labels={i:0 for i in range(3)}, display_node_weight=True, node_order=np.flip(np.arange(100)))
SVG(image)

[20]:

../../_images/tutorials_data_models_27_0.svg

Watts-Strogatz 模型

[21]:

adjacency = watts_strogatz(n=100, degree=6, prob=0.2)

[22]:

image = visualize_graph(adjacency, display_node_weight=True, node_size_max=10)
SVG(image)

[22]:

../../_images/tutorials_data_models_30_0.svg