Community discovery in networks with deep sparse filtering

In the past decade, network community discovery has attracted great attention from quite a few researchers, and community structure is one of the most significant properties in complex networks. This paper presents a novel method for network community discovery based on deep sparse filtering. The features of the network are extracted by sparse filtering, an unsupervised deep learning algorithm, from an efficient representation of the network. Consequently, extracted features are employed to partition the network. Experiment results on both synthetic and real-world network datasets indicate that the proposed algorithm especially based on S⌀rensen-Dice's similarity matrix representation of the network is efficient and it outperforms several state-of-art algorithms in discovering community structure.