PageRank算法

def create(q,graph,N):
    #compute Probability Matrix
    L = [[(1-q)/N]*N for i in range(N)]
    for node,edges in enumerate(graph):
        num_edge = len(edges)
        for each in edges:
            L[each][node] += q/num_edge
    return L
def transform(A):
    n,m = len(A),len(A[0])
    new_A = [[A[j][i] for j in range(n) ] for i in range(m)]
    return new_A
def mul(A,B):
    n = len(A)
    m = len(B[0])
    B = transform(B)
    next = [[0]*m for i in range(n)]
    for i in range(n):
        row = A[i]
        for j in range(m):
            col = B[j]
            next[i][j] = sum([row[k]*col[k] for k in range(n)])
    return next
def power(A,N):
    n = len(A)
    assert(len(A[0])==n)
    final_ans,temp = A,A
    N-=1
    while N>0:
        if N&1:
            final_ans = mul(final_ans,temp)
        temp = mul(temp,temp)
        N >>=1
    return final_ans
def PageRank(q,graph,N):
    X = [[1] for i in range(N)]
    A = create(q,graph,N)
    X = mul(power(A,20),X)
    return X
print(PageRank(0.85,[[1,2],[2],[0]],3))


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原文链接：https://blog.csdn.net/pp634077956/java/article/details/52604137

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穷人版PageRank算法的Python实现

#用于存储图
class Graph():
    def __init__(self):
        self.linked_node_map = {}#邻接表，
        self.PR_map ={}#存储每个节点的入度
    
    #添加节点
    def add_node(self, node_id):
        if node_id not in self.linked_node_map:
            self.linked_node_map[node_id] = set({})
            self.PR_map[node_id] = 0
        else:
            print("这个节点已经存在")
    
    #增加一个从Node1指向node2的边。允许添加新节点
    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)#为node1添加一个邻接节点，表示ndoe2引用了node1
    
    #计算pr
    def get_PR(self, epoch_num=10, d=0.5):#配置迭代轮数，以及阻尼系数
        for i in range(epoch_num):
            for node in self.PR_map:#遍历每一个节点
                self.PR_map[node] = (1-d) + d*sum([self.PR_map[temp_node] for temp_node in self.linked_node_map[node]])#原始版公式
            print(self.PR_map)
            

edges = [[1,2], [3,2], [3,5], [1,3], [2,3], [3, 1], [5,1]]#模拟的一个网页链接网络       
if __name__ == '__main__':
    graph = Graph()
    for edge in edges:
        graph.add_link(edge[0], edge[1])
    graph.get_PR()

原文链接:
https://zhuanlan.zhihu.com/p/81691075

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2020-05-11

标签：node,map,self,PR,range,算法,PageRank,def
来源： https://www.cnblogs.com/hany-postq473111315/p/12871080.html