A recursive neural network model for processing directed acyclic graphs with labeled edges

The recursive paradigm extends the neural network processing and learning algorithms to deal with structured inputs. In particular, recursive neural network (RNN) models have been proposed to process information coded as directed positional acyclic graphs (DPAGs) whose maximum node outdegree is known a priori. Unfortunately, the hypothesis of processing DPAGs having a given maximum node outdegree is sometimes too restrictive, being the nature of some real-world problems intrinsically disordered. In many applications the node outdegrees can vary considerably among the nodes in the graph, it may be unnatural to define a position for each child of a given node, and it may be necessary to prune some edges to reduce the number of the network parameters, which is proportional to the maximum node outdegree. In this paper, we proposed a new recursive neural network model which allows us to process directed acyclic graphs (DAGs) with labeled edges, relaxing the positional constraint and the correlated maximum outdegree limit. The effectiveness of the new scheme is experimentally tested on an image classification task. The results show that the new RNN model outperforms the standard RNN architecture, also allowing us to use a smaller number of free parameters