Recursive processing of cyclic graphs

IEEE Transactions on Neural Networks
M BianchiniFranco Scarselli

Abstract

Recursive neural networks are a powerful tool for processing structured data. According to the recursive learning paradigm, the input information consists of directed positional acyclic graphs (DPAGs). In fact, recursive networks are fed following the partial order defined by the links of the graph. Unfortunately, the hypothesis of processing DPAGs is sometimes too restrictive, being the nature of some real-world problems intrinsically cyclic. In this paper, a methodology is proposed, which allows us to process any cyclic directed graph. Therefore, the computational power of recursive networks is definitely established, also clarifying the underlying limitations of the model.

References

Sep 24, 2004·IEEE Transactions on Neural Networks·Hsien-Leing Tsai, Shie-Jue Lee
Nov 30, 2004·IEEE Transactions on Neural Networks·Alessio MicheliAlessandro Sperduti
Sep 27, 2005·Neural Networks : the Official Journal of the International Neural Network Society·M BianchiniF Scarselli
Feb 2, 2008·IEEE Transactions on Neural Networks·M HagenbuchnerAh Chung Tsoi
Feb 2, 2008·IEEE Transactions on Neural Networks·Siu-Yeung ChoAh Chung Tsoi
Feb 6, 2008·IEEE Transactions on Neural Networks·M Bianchini, M Gori
Feb 6, 2008·IEEE Transactions on Neural Networks·M BianchiniF Scarselli
Jan 1, 1997·IEEE Transactions on Neural Networks·A Sperduti, A Starita
Feb 8, 2008·IEEE Transactions on Neural Networks·P FrasconiA Sperduti

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Citations

Aug 27, 2013·Neural Networks : the Official Journal of the International Neural Network Society·Franco ScarselliLucia Di Noi
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Derek D Monner, James A Reggia
Jan 9, 2009·IEEE Transactions on Neural Networks·Franco ScarselliGabriele Monfardini
Feb 6, 2009·IEEE Transactions on Neural Networks·Alessio Micheli
Dec 2, 2010·IEEE Transactions on Neural Networks·Peng RenEdwin R Hancock

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