資源簡介
基于Python實現的Pagerank算法 算法結果輸出復雜網絡中每個節點的重要性
代碼片段和文件信息
#?-*-?coding:?UTF-8?-*-
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Created?on?17-12-3
@summary:?python?實現PageRank算法
@author:?dreamhome
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import?networkx?as?nx
def?read_graph_from_file(path):
????“““
????:param?path:?從文件中讀取圖結構
????:return:?Graph?graph
????“““
????#?定義圖
????graph?=?nx.Graph()
????#?獲取邊列表edges_list
????edges_list?=?[]
????#?開始獲取邊
????fp?=?open(path)
????edge?=?fp.readline().split()
????while?edge:
????????if?edge[0].isdigit()?and?edge[1].isdigit():
????????????edges_list.append((int(edge[0])?int(edge[1])))
????????edge?=?fp.readline().split()
????fp.close()
????#?為圖增加邊
????graph.add_edges_from(edges_list)
????return?graph
def?pagerank(graph?damping_factor?max_iterations?delta):
????“““
????pagerank算法
????:param?path:?圖
????:param?damping_factor:?阻尼系數
????:param?max_iterations:?最大迭代次數
????:param?delta:?算法終止條件
????:return:?返回每個節點的重要性
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????#?獲取圖屬性
????nodes?=?graph.nodes()
????graph_size?=?graph.number_of_nodes()
????#?每個節點賦予初始PR值
????page_rank?=?dict.fromkeys(nodes?1.0?/?graph_size)
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