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- W1569786639 abstract "Detection of clusters and communities in graphs is useful in a wide range of applications. In this paper we investigate the problem of detecting a clique embedded in a random graph. Recent results have demonstrated a sharp detectability threshold for a simple algorithm based on principal component analysis (PCA). Sparse PCA of the graph's modularity matrix can successfully discover clique locations where PCA-based detection methods fail. In this paper, we demonstrate that applying sparse PCA to low-rank approximations of the modularity matrix is a viable solution to the planted clique problem that enables detection of small planted cliques in graphs where running the standard semidefinite program for sparse PCA is not possible." @default.
- W1569786639 created "2016-06-24" @default.
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- W1569786639 date "2015-04-01" @default.
- W1569786639 modified "2023-09-23" @default.
- W1569786639 title "Planted clique detection below the noise floor using low-rank sparse PCA" @default.
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- W1569786639 doi "https://doi.org/10.1109/icassp.2015.7178667" @default.
- W1569786639 hasPublicationYear "2015" @default.
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