Spectral Clustering and Community Detection in Labeled Graphs
نویسندگان
چکیده
We study spectral clustering techniques to learn community structures in labeled random graphs where edge labels from a label set L = {1, ..., L} are drawn according to discrete probability distributions parametrized by community membership of the two end-nodes of the edge. This is a strict generalization of the standard stochastic block model for community detection.
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تاریخ انتشار 2015