计算机工程2018，Vol.44Issue(3)：220-225,232,7.DOI:10.3969/j.issn.1000-3428.2018.03.037
基于深度游走模型的标签传播社区发现算法
Label Propagation Community Discovery Algorithm Based on DeepWalk Model
摘要
Abstract
Aiming at the problem of low accuracy in traditional Label Propagation Algorithm(LPA),an improved label propagation algorithm based on DeepWalk model is proposed.Firstly,the algorithm takes the social network as the input of the DeepWalk model,samples the nodes in the network to get random sequences by means of a deep random walk,and uses SkipGram model to train the samples in neural network.Secondly,computes the kernel part of SkipGram model by hierarchical Softmax and obtains the feature vector of the nodes,and then calculates the similarity between the nodes.Finally,takes the similarity of the nodes as the weight during the label propagation procedure,and then gets the results of community detection.Experimental results on 6 real network dataset and synthetic dataset show that,compares with the traditional label propagation algorithm,the improved algorithm gets the higher accuracy,and especially when the nodes' number is more than 100 in real network dataset,the Q shows 10％ rise in improved algorithm.关键词
深度游走模型/随机序列/特征向量/SkipGram模型/节点相似度/传播迭代Key words
DeepWalk model/random sequence/feature vector/SkipGram model/node similarity/propagation iteration分类
信息技术与安全科学引用本文复制引用
冯曦,朱福喜,刘世超..基于深度游走模型的标签传播社区发现算法[J].计算机工程,2018,44(3):220-225,232,7.基金项目
国家自然科学基金(61272277). （61272277）
