The Power of Side-information in Subgraph Detection

Abstract : In this work, we tackle the problem of hidden community detection. We consider Belief Propagation (BP) applied to the problem of detecting a hidden Erd\H{o}s-R\'enyi (ER) graph embedded in a larger and sparser ER graph, in the presence of side-information. We derive two related algorithms based on BP to perform subgraph detection in the presence of two kinds of side-information. The first variant of side-information consists of a set of nodes, called cues, known to be from the subgraph. The second variant of side-information consists of a set of nodes that are cues with a given probability. It was shown in past works that BP without side-information fails to detect the subgraph correctly when an effective signal-to-noise ratio (SNR) parameter falls below a threshold. In contrast, in the presence of non-trivial side-information, we show that the BP algorithm achieves asymptotically zero error for any value of the SNR parameter. We validate our results through simulations on synthetic datasets as well as on a few real world networks.
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Contributeur : Arun Kadavankandy <>
Soumis le : lundi 6 mars 2017 - 14:07:57
Dernière modification le : jeudi 3 mai 2018 - 13:00:23
Document(s) archivé(s) le : mercredi 7 juin 2017 - 13:58:02


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  • HAL Id : hal-01394889, version 4
  • ARXIV : 1611.04847


Arun Kadavankandy, Konstantin Avrachenkov, Laura Cottatellucci, Rajesh Sundaresan. The Power of Side-information in Subgraph Detection. [Research Report] RR-8974, Inria Sophia Antipolis. 2017, pp.37. 〈hal-01394889v4〉



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