Subgraph Matching Using Graph Neural Network

  title={Subgraph Matching Using Graph Neural Network},
  author={GnanaJothi Raja Baskararaja and M. Manickavasagam},
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 subgraph in a graph. 


Publications referenced by this paper.
Showing 1-8 of 8 references

A Comparison between Recursive Neural Networks and Graph Neural Networks

The 2006 IEEE International Joint Conference on Neural Network Proceedings • 2006
View 1 Excerpt

Ant Colony Optimization for Multivalent Graph Matching Problems

O. Sammound, C. Solnon, K. Ghedira
2006. • 2006
View 1 Excerpt

Applications of Graph Neural Networks to Large-Scale Recommender Systems Some Results

A. Pucci, M. Gori, M. Hagenbuchner, F. Scarselli, A. C. Tsoi
Proceedings of International Multiconference on Computer Science and Information Technology, Vol. 1, No. 6-10, 2006, pp.189-195. • 2006
View 1 Excerpt

Subgraph Matching with Semidefinite Programming

Electronic Notes in Discrete Mathematics • 2003
View 1 Excerpt

Similar Papers

Loading similar papers…