Open AccessDeep Auto‐encoded Clustering Algorithm for Community Detection in Complex Networks
Author(s) -
Wang Feifan,
Zhang Baihai,
Chai Senchun
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2019.03.019
Subject(s) - cluster analysis , computer science , representation (politics) , key (lock) , artificial intelligence , spectral clustering , algorithm , decomposition , cornerstone , deep learning , pattern recognition (psychology) , data mining , machine learning , art , ecology , computer security , politics , political science , law , visual arts , biology
The prevalence of deep learning has inspired innovations in numerous research fields including community detection, a cornerstone in the advancement of complex networks. We propose a novel community detection algorithm called the Deep auto‐encoded clustering algorithm (DAC), in which unsupervised and sparse single autoencoders are trained and piled up one after another to embed key community information in a lower‐dimensional representation, such that it can be handled easier by clustering strategies. Extensive comparison tests undertaken on synthetic and real world networks reveal two advantages of the proposed algorithm: on the one hand, DAC shows higher precision than the k ‐means community detection method benefiting from the integration of sparsity constraints. On the other hand, DAC runs much faster than the spectral community detection algorithm based on the circumvention of the time‐consuming eigenvalue decomposition procedure.