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V. Di Massa, G. Monfardini, L. Sarti, C. F. Scarselli, M. Maggini and M. Gori, “A Comparision between Recursive Neural Networks and Graph Neural Networks,” In-ternational Joint Conference on Neural Networks, 2006, pp. 778-785.

has been cited by the following article:

• TITLE: Subgraph Matching Using Graph Neural Network

  AUTHORS: GnanaJothi Raja Baskararaja, MeenaRani Sundaramoorthy Manickavasagam

  KEYWORDS: Subgraph Matching; Graph Neural Network; Backpropagation; Recurrent Neural Network; Feedforward Neural Network

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.4 No.4, November 28, 2012

  ABSTRACT: Subgraph matching problem is identifying a target subgraph in a graph. Graph neural network (GNN) is an artificial neural network model which is capable of processing general types of graph structured data. A graph may contain many subgraphs isomorphic to a given target graph. In this paper GNN is modeled to identify a subgraph that matches the target graph along with its characteristics. The simulation results show that GNN is capable of identifying a target sub-graph in a graph.