【网络】基于标签的LPA算法的python3版本

LPA算法python2版本见：http://blog.csdn.net/ztf312/article/details/78710501。

下面是python3版本。

# -*- coding: utf-8 -*-"""Created on Mon Dec  4 15:47:35 2017@author: ztf"""# -*- coding: UTF-8 -*-"""Created on 17-11-28@summary: 实现传统标签传播算法LPA@author: dreamhome"""import randomimport networkx as nximport matplotlib.pyplot as pltfrom nets import *def generate_graph(G):    """    :return: Graph graph    """    # 定义图    graph = G    # 给每个节点增加标签    for node, data in graph.nodes_iter(True):        data['label'] = node                return graphdef lpa(graph):    """    标签传播算法 使用异步更新方式    :param graph:    :return:    """    def estimate_stop_condition():        """        算法终止条件：所有节点的标签与大部分邻居节点标签相同或者迭代次数超过指定值则停止        :return:        """        for node in graph.nodes_iter():            count = {}            for neighbor in graph.neighbors_iter(node):                neighbor_label = graph.node[neighbor]['label']                count[neighbor_label] = count.setdefault(                    neighbor_label, 0) + 1            # 找到计数值最大的label            count_items = count.items()            count_items = sorted(count_items, key=lambda x: x[1], reverse=True)            labels = [k for k, v in count_items if v == count_items[0][1]]            # 当节点标签与大部分邻居节点标签相同时则达到停止条件            if graph.node[node]['label'] not in labels:                return False        return True    loop_count = 0    # 迭代标签传播过程    while True:        loop_count += 1        print('迭代次数', loop_count)        for node in graph.nodes_iter():            count = {}            for neighbor in graph.neighbors_iter(node):                neighbor_label = graph.node[neighbor]['label']                count[neighbor_label] = count.setdefault(                    neighbor_label, 0) + 1            # 找到计数值最大的标签            count_items = count.items()            # print count_items            count_items = sorted(count_items, key=lambda x: x[1], reverse=True)            labels = [(k, v) for k, v in count_items if v == count_items[0][1]]            # 当多个标签最大计数值相同时随机选取一个标签            label = random.sample(labels, 1)[0][0]            graph.node[node]['label'] = label        if estimate_stop_condition() is True or loop_count >= 10:            print('complete')            returnif __name__ == "__main__":#    path = "/home/dreamhome/network-datasets/dolphins/out.dolphins"#    graph = read_graph_from_file(path)    G = WS(100, 2, 0.1)    graph = generate_graph(G)    lpa(graph)    # 根据算法结果画图    node_color = [float(graph.node[v]['label']) for v in graph]    nx.draw_networkx(graph, node_color=node_color)    plt.show()    labels = set([float(graph.node[v]['label']) for v in graph])    community = dict()    print(labels)    for l in labels:        community[l] = [v for v in graph if float(graph.node[v]['label'])==l]        print(l, ":", community[l])