8 research outputs found
Le Pouvoir d'Information Supplementaire en Detection des Sousgraphes
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
Almost exact recovery in noisy semi-supervised learning
This paper investigates noisy graph-based semi-supervised learning or
community detection. We consider the Stochastic Block Model (SBM), where, in
addition to the graph observation, an oracle gives a non-perfect information
about some nodes' cluster assignment. We derive the Maximum A Priori (MAP)
estimator, and show that a continuous relaxation of the MAP performs almost
exact recovery under non-restrictive conditions on the average degree and
amount of oracle noise. In particular, this method avoids some pitfalls of
several graph-based semi-supervised learning methods such as the flatness of
the classification functions, appearing in the problems with a very large
amount of unlabeled data
The Power of Side-Information in Subgraph Detection
International audienceIn this work, we tackle the problem of hidden community detection. We consider Belief Propagation (BP) applied to the problem of detecting a hidden Erdos-Renyi (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 twokinds 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 a so-called 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 a suitably defined phase-transition parameter. We validate our results on synthetic datasets and a few real world networks