title: Recursive processing of cyclic graphs.
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.
authors.affiliation: Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Siena, 1-53100, Siena, Italy. monica@dii.unisi.it
authors.forename: Monica
authors.initials: M
authors.lastname: Bianchini
authors.forename: Marco
authors.initials: M
authors.lastname: Gori
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Sarti
authors.forename: Franco
authors.initials: F
authors.lastname: Scarselli