Create lb.py

Author: zhaozhiyong19890102

Chapter_13 LabelPropagation/lb.py

@@ -0,0 +1,117 @@
+# coding:UTF-8
+'''
+Date:20160805
+@author: zhaozhiyong
+'''
+import string
+
+def loadData(filePath):
+    '''
+    input:  filePath(string)文件的存储位置
+    output: vector_dict(dict)节点：社区
+            edge_dict(dict)存储节点之间的边和权重
+    '''
+    f = open(filePath)
+    vector_dict = {}  # 存储节点
+    edge_dict = {}  # 存储边
+    for line in f.readlines():
+        lines = line.strip().split("\t")
+
+        for i in xrange(2):
+            if lines[i] not in vector_dict:  # 节点已存储
+                # 将节点放入到vector_dict中，设置所属社区为其自身
+                vector_dict[lines[i]] = string.atoi(lines[i])
+                # 将边放入到edge_dict
+                edge_list = []
+                if len(lines) == 3:
+                    edge_list.append(lines[1 - i] + ":" + lines[2])
+                else:
+                    edge_list.append(lines[1 - i] + ":" + "1")
+                edge_dict[lines[i]] = edge_list
+            else:  # 节点未存储
+                edge_list = edge_dict[lines[i]]
+                if len(lines) == 3:
+                    edge_list.append(lines[1 - i] + ":" + lines[2])
+                else:
+                    edge_list.append(lines[1 - i] + ":" + "1")
+                edge_dict[lines[i]] = edge_list
+    f.close()
+    return vector_dict, edge_dict
+
+def get_max_community_label(vector_dict, adjacency_node_list):
+    '''得到相邻接的节点中标签数最多的标签
+    input:  vector_dict(dict)节点：社区
+            adjacency_node_list(list)节点的邻接节点
+    output: 节点所属的社区
+    '''
+    label_dict = {}
+    for node in adjacency_node_list:
+        node_id_weight = node.strip().split(":")
+        node_id = node_id_weight[0]#邻接节点
+        node_weight = string.atoi(node_id_weight[1])#与邻接节点之间的权重
+        if vector_dict[node_id] not in label_dict:
+            label_dict[vector_dict[node_id]] = node_weight
+        else:
+            label_dict[vector_dict[node_id]] += node_weight
+
+    # 找到最大的标签
+    sort_list = sorted(label_dict.items(), key=lambda d: d[1], reverse=True)
+    return sort_list[0][0]
+
+def check(vector_dict, edge_dict):
+    '''检查是否满足终止条件
+    input:  vector_dict(dict)节点：社区
+            edge_dict(dict)存储节点之间的边和权重
+    output: 是否需要更新
+    '''
+    for node in vector_dict.keys():
+        adjacency_node_list = edge_dict[node]  # 与节点node相连接的节点
+        node_label = vector_dict[node]  # 节点node所属社区
+        label = get_max_community_label(vector_dict, adjacency_node_list)
+        if node_label == label: # 对每个节点，其所属的社区标签是最大的
+            continue
+        else:
+            return 0
+    return 1
+
+def label_propagation(vector_dict, edge_dict):
+    '''标签传播
+    input:  vector_dict(dict)节点：社区
+            edge_dict(dict)存储节点之间的边和权重
+    output: vector_dict(dict)节点：社区
+    '''
+    # 初始化，设置每个节点属于不同的社区
+    t = 0
+    # 以随机的次序处理每个节点
+    while True:
+        if (check(vector_dict, edge_dict) == 0):
+            t = t + 1
+            print "iteration: ", t
+            # 对每一个node进行更新
+            for node in vector_dict.keys():
+                adjacency_node_list = edge_dict[node] # 获取节点node的邻接节点
+                vector_dict[node] = get_max_community_label(vector_dict, adjacency_node_list)
+            print vector_dict
+        else:
+            break
+    return vector_dict
+
+def save_result(file_name, vec_new):
+    f_result = open(file_name, "w")
+    for key in vec_new.keys():
+        f_result.write(str(key) + "\t" + str(vec_new[key]) + "\n")   
+    f_result.close()
+
+
+if __name__ == "__main__":
+    # 1、导入数据
+    print "----------1.load data ------------"
+    vector_dict, edge_dict = loadData("cd_data.txt")
+    print "original community: \n", vector_dict
+    # 2、利用label propagation算法进行社区划分
+    print "----------2.label propagation ------------"
+    vec_new = label_propagation(vector_dict, edge_dict)
+    # 3、保存最终的社区划分的结果
+    print "----------3.save result ------------"
+    save_result("result1", vec_new)
+    print "final_result:", vec_new