距离
此笔记本演示了图中节点之间距离（以跳数计）的计算。
[1]:
from IPython.display import SVG
[2]:
import numpy as np
[3]:
from sknetwork.data import miserables, painters, movie_actor
from sknetwork.path import get_distances
from sknetwork.visualization import visualize_graph, visualize_bigraph
from sknetwork.utils import bipartite2undirected
图
[4]:
graph = miserables(metadata=True)
adjacency = graph.adjacency
names = graph.names
position = graph.position
[5]:
source = np.flatnonzero(names=='Cosette')
distances = get_distances(adjacency, source)
[6]:
image = visualize_graph(adjacency, position, names, scores=-distances, seeds=[source], scale=1.5)
SVG(image)
[6]:
有向图
[7]:
graph = painters(metadata=True)
adjacency = graph.adjacency
names = graph.names
position = graph.position
[8]:
# distances from Paul Cezanne
source = np.flatnonzero(names=='Paul Cezanne')
distances = get_distances(adjacency, source)
[9]:
# distances of unreachable nodes (for better visualization)
distances[distances < 0] = 5
[10]:
image = visualize_graph(adjacency, position, names, scores=-distances, seeds=[source])
SVG(image)
[10]:
二部图
[11]:
graph = movie_actor(metadata=True)
biadjacency = graph.biadjacency
names_row = graph.names_row
names_col = graph.names_col
[12]:
source_row = np.flatnonzero(np.isin(names_row, ['Drive', 'The Grand Budapest Hotel']))
distances_row, distances_col = get_distances(biadjacency, source_row=source_row)
[13]:
image = visualize_bigraph(biadjacency, names_row, names_col, scores_row=-distances_row, scores_col=-distances_col,
seeds_row=source_row)
SVG(image)
[13]:
