利用scikit-network 进行 pagenode Ranking

scikit-network介绍：

scikit-network - 知乎

pageRank/nodeRank介绍：

图上的node ranking问题 - 知乎

[论文阅读] PageRank Algorithm - 知乎

from sknetwork.ranking import PageRank
from sknetwork.data import house
import pandas as pd 
import numpy as np 
from sknetwork.ranking import top_k
pagerank = PageRank()
adjacency = house()
# 0节点的权重最大，注意所有节点权重之和为1
seeds = {0: 1}
scores = pagerank.fit_transform(adjacency, seeds)
np.round(scores, 2)
# 输出权重最大的节点
top_k(scores, k=2)

# 展示数据的graph
from IPython.display import SVG
import numpy as np
from sknetwork.data import house, bow_tie, karate_club, miserables, painters, hourglass, star_wars, movie_actor
from sknetwork.visualization import svg_graph, svg_digraph, svg_bigraph
graph = house(metadata=True)
adjacency = graph.adjacency
position = graph.position
image = svg_graph(adjacency, position, scale=0.5)
SVG(image)

参考：

利用python-sknetwork进行图聚类/社区发现 - 知乎