A. Pucci, M. Gori, M. Hagenbuchner, F. Scarselli and A. C. Tsoi, “Applications of Graph Neural Networks to LargeScale Recommender Systems Some Results,” Proceedings of International Multiconference on Computer Science and Information Technology, Vol. 1, No. 6-10, 2006, pp.189-195. - References

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A. Pucci, M. Gori, M. Hagenbuchner, F. Scarselli and A. C. Tsoi, “Applications of Graph Neural Networks to LargeScale Recommender Systems Some Results,” Proceedings of International Multiconference on Computer Science and Information Technology, Vol. 1, No. 6-10, 2006, pp.189-195.

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.

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