Document Type

Journal Article

Department/Unit

Office of the Vice-President (Research and Development)

Abstract

Many underlying relationships among data in several areas of science and engineering, e.g. computer vision, molecular chemistry, molecular biology, pattern recognition, 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 the graph domain. This GNN model, which can directly process most of the practically useful types of graphs, e.g. acyclic, cyclic, directed, un-directed, implements a transduction function $\tau(\BG,n)\in\R^m$ that maps a graph $\BG$ 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. Computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and demonstrate its generalization capability

Publication Year

2009

Journal Title

IEEE Transaction on Neural Networks

Volume number

20

Issue number

1

Publisher

IEEE Press

First Page (page number)

81

Last Page (page number)

102

Referreed

1

Funder

Australian research Council

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