Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can beâ€¦ (More)

In several applications the information is naturally represented by graphs. Traditional approaches cope with graphical data structures using a preprocessing phase which transforms the graphs into aâ€¦ (More)

In this paper, we will consider the approximation properties of a recently introduced neural network model called graph neural network (GNN), which can be used to process-structured data inputs,â€¦ (More)

In this paper, two recently developed connectionist models for learning from relational or graph-structured data, i.e. Relational Neural Networks (RelNNs) and Graph Neural Networks (GNNs), areâ€¦ (More)

The 2006 IEEE International Joint Conference onâ€¦

2006

Recursive neural networks (RNNs) and graph neural networks (GNNs) are two connectionist models that can directly process graphs. RNNs and GNNs exploit a similar processing framework, but they can beâ€¦ (More)

In the last decade, connectionist models have been proposed that can process structured information directly. These methods, which are based on the use of graphs for the representation of the dataâ€¦ (More)

This paper proposes a new neural network approach to the classification of vehicles in image sequences recorded by a stationary camera. The novelty consists in organizing the tracking data intoâ€¦ (More)