This function applies t-SNE, dimensionality reduction algorithm, on centrality measures.
tsne.centralities(x, dims = 2, perplexity = 5, scale = TRUE)a list containg the computed cetrality values
integer; number of the outpu dimensions(default=2)
numeric; A flexible measure of the efficient number of neighbors. The performance of SNE is fairly robust to changes in the perplexity, and typical values are between 5 and 50.(default=5)
Whether the centrality values should be scaled or not(default=TRUE)
It resturns cost plot of tsne results which displays centralities in order of their corresponding costs.
t-SNE is a non-linear dimensionality reduction algorithm used for exploring high-dimensional data. Here, It maps multi-dimensional centrality measure data to less dimensions suitable to work with it.
van der Maaten, L. (2014). Accelerating t SNE using Tree Based Algorithms. Journal of Machine Learning Research, 15, 3221<U+2013>3245. Van Der Maaten, L. J. P., & Hinton, G. E. (2008). Visualizing high dimensional data using t sne. Journal of Machine Learning Research, 9, 2579<U+2013>2605.