A method based on compressive sensing to detect community structure using deep belief network

摘要

A deep learning scheme based on compressive sensing to detect community structure of large-scale social network is presented. Our contributions in this work are as follows: First, we reduced the high-dimensional feature of social media data via compressive sensing by using random measurement matrix; Second, deep belief network is employed to learn unsupervised from the low-dimensional samples; Finally the model is fine-tuned by supervised learning from a small scale sample sets with class labels. The effectiveness of the proposed scheme is confirmed by the experiment results.