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## Looks good. The first impression: the paper is similar to Kleinberg's [masterpiece](https://www.cs.cornell.edu/home/kleinber/nips15.pdf). Also, one column format is more agreable to read for normal humans than 2-colums.
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### General: A very simple and very scalable algorithm with theoretical guarantees to compute an approximation of the closeness centrality for each node in the graph. Maybe the framework can be adapted to solve other problems. In particular, "Hoeffding's theorem" is very handy. ### Parallelism: Note that the algorithm is embarrassingly parallel (the loop over the $k$ reference nodes can be done in parallel with almost no additional work). ### Experiments: It would be nice to have some experiments on large real-world graphs. An efficient (and parallel) implementation of the algorithm is available here: https://github.com/maxdan94/BFS ### Related work: More recent related work by Paolo Boldi and Sebastiano Vigna: - http://mmds-data.org/presentations/2016/s-vigna.pdf - https://arxiv.org/abs/1011.5599 - https://papers-gamma.link/paper/37 It also gives an approximation of the centrality for each node in the graph and it also scales to huge graphs. Which algorithm is "the best"?
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