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Generative model selection using a scalable and size-independent complex network classifier
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10.1063/1.4840235
/content/aip/journal/chaos/23/4/10.1063/1.4840235
http://aip.metastore.ingenta.com/content/aip/journal/chaos/23/4/10.1063/1.4840235

Figures

Image of FIG. 1.
FIG. 1.

The methodology of learning a network classifier.

Image of FIG. 2.
FIG. 2.

The graphlets with three and four nodes.

Image of FIG. 3.
FIG. 3.

Precision of GMSCN compared to baseline method for different generative models.

Image of FIG. 4.
FIG. 4.

Recall of GMSCN compared to baseline method for different generative models.

Image of FIG. 5.
FIG. 5.

Accuracy of GMSCN for different network sizes.

Image of FIG. 6.
FIG. 6.

Robustness of the different classification methods with respect to random edge rewiring.

Tables

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Table I.

Precision, Recall, and Accuracy of GMSCN for different generative models.

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Table II.

Precision, Recall, and Accuracy of the baseline method for different generative models.

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Table III.

Precision and Recall of GMSCN for networks of different sizes.

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Table IV.

The results of GMSCN after excluding the features of degree distribution.

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/content/aip/journal/chaos/23/4/10.1063/1.4840235
2013-12-05
2014-04-21
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Generative model selection using a scalable and size-independent complex network classifier
http://aip.metastore.ingenta.com/content/aip/journal/chaos/23/4/10.1063/1.4840235
10.1063/1.4840235
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