基于平滑L1范数的深度稀疏自动编码器社区识别算法

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-01-28 Cooperative journals: 《计算机应用研究》

张军祥 李书琴 刘斌

Abstract： In the age of big data, it is increasingly difficult to make the community structure mining of large-scale complex networks by using the traditional community discovery algorithm and the accuracy rate is low. Therefore, this research come up with L_1-ECDA, a community discovery algorithm for deep sparse self-encoder based on smooth L_1 norm. This algorithm preprocessed the adjacency matrix of the network diagram with the method based on s Jump; then it established the deep sparse self-encoder based on smooth L_1 norm and get the low dimensional characteristic matrix by training the similarity matrix of the network graph; Finally, it get the network community structure by clustering the low-dimensional feature matrix through the K-means algorithm. Experiments on simulated network and real network data set show that the algorithm of L_1-ECDA improves the accuracy of community recognition effectively. Its accuracy rate is 4% higher than the DBCS algorithm on average, and 5.4% higher than Deepwalk algorithm and CoDDA algorithm on average.

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