Feature dimension reduction in the community detection is an important research topic in complex networks and has attracted many research efforts in recent years. However, most of existing algorithms developed for this purpose take advantage of classical mechanisms, which may be long experimental, time-consuming, and ineffective for complex networks. To this purpose, a novel deep sparse autoencoder for community detection, named DSACD, is proposed in this paper. In DSACD, a similarity matrix is constructed to reveal the indirect connections between nodes and a deep sparse automatic encoder based on unsupervised learning is designed to reduce the dimension and extract the feature structure of complex networks. During the process of back propagation, L-BFGS avoid the calculation of Hessian matrix which can increase the calculation speed. The performance of DSACD is validated on synthetic and real-world networks. Experimental results demonstrate the effectiveness of DSACD and the systematic comparisons with four algorithms confirm a significant improvement in terms of three index F_same, NMI, and modularity Q. Finally, these achieved received signal strength indication (RSSI) data set can be aggregated into 64 correct communities, which further confirms its usability in indoor location systems.

社区检测中特征维的减少是复杂网络中的重要研究课题，近年来引起了很多研究工作。但是，为此目的开发的大多数现有算法都利用了经典机制，这些机制可能需要长时间的实验，耗时且对复杂的网络无效。为此，本文提出了一种新的用于社区检测的深度稀疏自动编码器，称为DSACD。在DSACD中，构造了一个相似性矩阵以揭示节点之间的间接连接，并设计了一种基于无监督学习的深度稀疏自动编码器，以减小维度并提取复杂网络的特征结构。在反向传播过程中，L-BFGS避免了Hessian矩阵的计算，从而提高了计算速度。DSACD的性能已在综合和现实网络中得到验证。实验结果证明了DSACD的有效性，并且与四种算法的系统比较证实了在三个指标方面的显着改进˚F_相同，NMI，和模块化Q。最后，这些获得的接收信号强度指示（RSSI）数据集可以汇总到64个正确的社区中，这进一步证实了其在室内定位系统中的可用性。