@inproceedings{2cd67e3ebad34214abaf0c8a0400a3f9,
title = "A spectral algorithm with additive clustering for the recovery of overlapping communities in networks",
abstract = "This paper presents a novel spectral algorithm with additive clustering, designed to identify overlapping communities in networks. The algorithm is based on geometric properties of the spectrum of the expected adjacency matrix in a random graph model that we call stochastic blockmodel with overlap (SBMO). An adaptive version of the algorithm, that does not require the knowledge of the number of hidden communities, is proved to be consistent under the SBMO when the degrees in the graph are (slightly more than) logarithmic. The algorithm is shown to perform well on simulated data and on real-world graphs with known overlapping communities.",
author = "Emilie Kaufmann and Thomas Bonald and Marc Lelarge",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 27th International Conference on Algorithmic Learning Theory, ALT 2016 ; Conference date: 19-10-2016 Through 21-10-2016",
year = "2016",
month = jan,
day = "1",
doi = "10.1007/978-3-319-46379-7\_24",
language = "English",
isbn = "9783319463780",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "355--370",
editor = "Simon, \{Hans Ulrich\} and Sandra Zilles and Ronald Ortner",
booktitle = "Algorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings",
}