Abstract
Stochastic graph grammars are probabilistic models suitable for modeling relational data, complex organic molecules, social networks, and various other data distributions [1]. In this paper, we demonstrate that such grammars can be used to reveal useful information about the underlying distribution. In particular, we demonstrate techniques for estimating the expected number of nodes, the expected number of edges, and the expected average node degree, in a graph sampled from the distribution. These estimation techniques use the underlying grammar, and hence do not require sampling. Experimental results indicate that our estimation techniques are reasonably accurate.
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References
Rozenberg, G. (ed.): Handbook of Graph Grammars and Computing by Graph Transformations. Foundations, vol. 1. World Scientific, Singapore (1997)
West, D.B.: Introduction to Graph Theory, 2nd edn. Prenctice Hall, Upper Saddle River (2001)
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© 2008 Springer-Verlag Berlin Heidelberg
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Mukherjee, S., Oates, T. (2008). Estimating Graph Parameters Using Graph Grammars. In: Clark, A., Coste, F., Miclet, L. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2008. Lecture Notes in Computer Science(), vol 5278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88009-7_26
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DOI: https://doi.org/10.1007/978-3-540-88009-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88008-0
Online ISBN: 978-3-540-88009-7
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