PageRank Nibble on the sparse directed stochastic block model

Sayan Banerjee; Prabhanka Deka; Mariana Olvera-Cravioto

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arXiv · 2303.06699

PageRank Nibble on the sparse directed stochastic block model

Sayan Banerjee·Prabhanka Deka·Mariana Olvera-Cravioto

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Abstract

We present new results on community recovery based on the PageRank Nibble algorithm on a sparse directed stochastic block model (dSBM). Our results are based on a characterization of the local weak limit of the dSBM and the limiting PageRank distribution. This characterization allows us to estimate the probability of misclassification for any given connection kernel and any given number of seeds (vertices whose community label is known). The fact that PageRank is a local algorithm that can be efficiently computed in both a distributed and asynchronous fashion, makes it an appealing method for identifying members of a given community in very large networks where the identity of some vertices is known.

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BibTeX RIS

Sayan Banerjee, Prabhanka Deka, Mariana Olvera-Cravioto. 2023-03-12. PageRank Nibble on the sparse directed stochastic block model. https://arxiv.org/abs/2303.06699

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