Spectral Clustering of Graphs with the Bethe Hessian

NeurIPS 2014 Alaa SaadeFlorent KrzakalaLenka Zdeborová

Spectral clustering is a standard approach to label nodes on a graph by studying the (largest or lowest) eigenvalues of a symmetric real matrix such as e.g. the adjacency or the Laplacian. Recently, it has been argued that using instead a more complicated, non-symmetric and higher dimensional operator, related to the non-backtracking walk on the graph, leads to improved performance in detecting clusters, and even to optimal performance for the stochastic block model... (read more)

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