A new model for learning in graph domains

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 a set of flat vectors. However, in this way, important topological information may be lost and the achie...

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Bibliographic Details
Published inProceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005 Vol. 2; pp. 729 - 734 vol. 2
Main Authors Gori, M., Monfardini, G., Scarselli, F.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Application software
Data structures
Encoding
Focusing
Machine learning
Machine learning algorithms
Neural networks
Recurrent neural networks
Software engineering
Tree graphs
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Summary: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 a set of flat vectors. However, in this way, important topological information may be lost and the achieved results may heavily depend on the preprocessing stage. This paper presents a new neural model, called graph neural network (GNN), capable of directly processing graphs. GNNs extends recursive neural networks and can be applied on most of the practically useful kinds of graphs, including directed, undirected, labelled and cyclic graphs. A learning algorithm for GNNs is proposed and some experiments are discussed which assess the properties of the model.
ISBN:0780390482
9780780390485
ISSN:2161-4393
DOI:10.1109/IJCNN.2005.1555942