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Non-negative sparse autoencoder neural networks for the detection of overlapping, hierarchical communities in
Sarah Michele Rajtmajer1, Brian Smith, Shashi Phoha
1Applied Research Laboratory, The Pennsylvania State University, P.O. Box 30, State College, Pennsylvania 16804, USA. smr48@psu.edu
Abstract:
We propose the first use of a non-negative sparse autoencoder (NNSAE) neural network for community structure detection in complex networks. The NNSAE learns a compressed representation of a set of fixed-length, weighted random walks over the network, and communities are detected as subsets of network nodes corresponding to non-negligible elements of the basis vectors of this compression. The NNSAE model is efficient and online. When utilized for community structure detection, it is able to uncover potentially overlapping and hierarchical community structure in large networks.