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In this article, several research perspectives in random matrix theory applied to graph theory at large are discussed. Specific focus will be made on the spectrum analysis of the adja- cency or Laplacian matrices of large dimensional graphs for community detection in networks, of kernel random matrices for clustering in large datasets, along with applications to neural networks.
random matrix theory, spectral clustering, neural networks, graphs, random graphs.
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