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Edge based stochastic block model statistical inference

Louis Duvivier 1 Rémy Cazabet 1 Céline Robardet 1 
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Community detection in graphs often relies on ad hoc algorithms with no clear specification about the node partition they define as the best, which leads to uninterpretable communities. Stochastic block models (SBM) offer a framework to rigorously define communities, and to detect them using statistical inference method to distinguish structure from random fluctuations. In this paper, we introduce an alternative definition of SBM based on edge sampling. We derive from this definition a quality function to statistically infer the node partition used to generate a given graph. We then test it on synthetic graphs, and on the zachary karate club network.
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https://hal.archives-ouvertes.fr/hal-03340026
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Submitted on : Thursday, September 9, 2021 - 9:37:05 PM
Last modification on : Friday, September 30, 2022 - 11:34:16 AM
Long-term archiving on: : Saturday, December 11, 2021 - 7:26:47 AM

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Louis Duvivier, Rémy Cazabet, Céline Robardet. Edge based stochastic block model statistical inference. International Conference on Complex Networks and Their Applications, pp.462-473, 2021, ⟨10.1007/978-3-030-65351-4_37⟩. ⟨hal-03340026⟩

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