LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

A new deep sparse autoencoder for community detection in complex networks

Photo from wikipedia

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… Click to show full abstract

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.

Keywords: sparse autoencoder; deep sparse; community detection; complex networks

Journal Title: EURASIP Journal on Wireless Communications and Networking
Year Published: 2020

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.