title: The graph neural network model.
abstract: 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 be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) is an element of IR(m) that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.
authors.affiliation: Faculty of Information Engineering, University of Siena, Siena 53100, Italy. franco@dii.unisi.it
authors.forename: Franco
authors.initials: F
authors.lastname: Scarselli
authors.forename: Marco
authors.initials: M
authors.lastname: Gori
authors.forename: Ah Chung
authors.initials: AC
authors.lastname: Tsoi
authors.forename: Markus
authors.initials: M
authors.lastname: Hagenbuchner
authors.forename: Gabriele
authors.initials: G
authors.lastname: Monfardini
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D020407:Internet; D016014:Linear Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D012044:Regression Analysis; D015203:Reproducibility of Results