PageRank 的随机浏览模型代码

具体的概念网上均可搜到，仅展示代码实现

有向图.jpg

class Graph:
    def __init__(self):
        self.linked_node_map = {}  # 邻接表\
        self.linked_node_map_f ={}
        self.PR_map_1 = {}
        self.PR_map = {}  # 存储重要性

    def add_node(self, node_id):
        if node_id not in self.linked_node_map:
            self.linked_node_map[node_id] = set({})
        if node_id not in self.linked_node_map_f:
            self.linked_node_map_f[node_id] = set({})
        self.PR_map[node_id] = 0
        self.PR_map_1[node_id] = 0

        # else:
        #     print("此节点已存在")

    def add_link(self, node1, node2):
        if node1 not in self.linked_node_map:
            self.add_node(node1)
        if node2 not in self.linked_node_map:
            self.add_node(node2)
        self.linked_node_map[node1].add(node2)
        self.linked_node_map_f[node2].add(node1)

    # 计算pr
    def get_PR(self, epoch_num=100, d=0.85):
        L=len(self.linked_node_map)
        for j in range(epoch_num):
            if j == 0:
                for node in self.PR_map_1:
                    self.PR_map_1[node] = 1 / (len(self.linked_node_map))
            else:
                self.PR_map_1 = self.PR_map
            sum1 = 0
            for node in self.PR_map:
                list1 = list(self.linked_node_map_f[node])
                for i in list1:
                    sum1 += self.PR_map_1[i] / len(self.linked_node_map[i])
                self.PR_map[node] = ((1 - d) / L) + d * sum1
                sum1 = 0
        print(self.PR_map)



edges = [[1, 2], [1, 3], [1, 4], [2, 1], [2, 4], [3, 1], [4, 2], [4, 3]]


if __name__ == "__main__":
    graph = Graph()
    for edge in edges:
        graph.add_link(edge[0], edge[1])  # node1，node2
    print("邻接表：", graph.linked_node_map)
    print("表示入度的表：", graph.linked_node_map_f)
    graph.get_PR()

运行结果

运行结果1.png

对比networkx库中的 nx.pagerank(G, alpha=0.85,max_iter=100)方法

import networkx as nx

# 创建有向图
G = nx.DiGraph()
# 有向图之间边的关系
edges = [("A", "B"), ("A", "C"), ("A", "D"), ("B", "A"), ("B", "D"), ("C", "A"), ("D", "B"), ("D", "C")]
for edge in edges:
    G.add_edge(edge[0], edge[1])

page_rank_list = nx.pagerank(G, alpha=0.85,max_iter=100)

print("page_rank value：", page_rank_list)

运行结果

运行结果2.png