# Sublinear Algorithms for Local Graph Centrality Estimation

@article{Bressan2018SublinearAF, title={Sublinear Algorithms for Local Graph Centrality Estimation}, author={Marco Bressan and Enoch Peserico and Luca Pretto}, journal={2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS)}, year={2018}, pages={709-718} }

We study the complexity of local graph centrality estimation, with the goal of approximating the centrality score of a given target node while exploring only a sublinear number of nodes/arcs of the graph and performing a sublinear number of elementary operations. We develop a technique, that we apply to the PageRank and Heat Kernel centralities, for building a low-variance score estimator through a local exploration of the graph. We obtain an algorithm that, given any node in any graph of m…

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