graph.kmeans

<code>graph.kmeans</code> clusters graphs following a k-means algorithm based on the
Jensen-Shannon divergence between the spectral densities of the graphs.

k-means

Contains statistical methods to analyze graphs, such as graph parameter estimation,
model selection based on the Graph Information Criterion, statistical tests to discriminate two or more populations of graphs,
correlation between graphs, and clustering of graphs.
References: Takahashi et al. (2012) <doi:10.1371/journal.pone.0049949>, Fujita et al. (2017) <doi:10.3389/fnins.2017.00066>,
Fujita et al. (2017) <doi:10.1016/j.csda.2016.11.016>, Tang et al. (2017) <doi:10.3150/15-BEJ789>,
Tang et al. (2017) <doi:10.1080/10618600.2016.1193505>, Ghoshdastidar et al. (2017) <arXiv:1705.06168>,
Ghoshdastidar et al. (2017) <arXiv:1707.00833>, Cerqueira et al. (2017) <doi:10.1109/TNSE.2017.2674026>,
Fraiman and Fraiman (2018) <doi:10.1038/s41598-018-23152-5>, Fujita et al. (2019) <doi:10.1093/comnet/cnz028>.

Andre Fujita

statGraph

Statistical Methods for Graphs

Diogo R. da Costa

Taiane C. Ramos

Grover E. Castro Guzman

Suzana S. Santos

Eduardo S. Lira

graph.kmeans function

<dl><dt>x</dt>
<dd>a list containing the graphs or their adjacency matrices to be
clustered.</dd>
<dt>k</dt>
<dd>an integer specifying the number of clusters.</dd>
<dt>nstart</dt>
<dd>the number of trials of k-means clusterizations. The algorithm
returns the clusterization with the best silhouette.</dd></dl>

Arguments

K-means for Graphs — graph.kmeans

<dl>

<dt>x</dt>
<dd>a list containing the graphs or their adjacency matrices to be
clustered.</dd>


<dt>k</dt>
<dd>an integer specifying the number of clusters.</dd>


<dt>nstart</dt>
<dd>the number of trials of k-means clusterizations. The algorithm
returns the clusterization with the best silhouette.</dd>

</dl>

graph.kmeans: K-means for Graphs

Description

Usage

Value

Arguments

References

Examples