The Power of Side-information in Subgraph Detection

TL;DR

This paper demonstrates that incorporating side-information into belief propagation significantly improves hidden subgraph detection, enabling accurate detection even at low signal-to-noise ratios, validated through synthetic and real data.

Contribution

The paper introduces two BP-based algorithms that leverage different types of side-information to enhance hidden subgraph detection, surpassing previous limitations.

Findings

BP with side-information achieves zero error at any SNR

Algorithms outperform standard BP without side-information

Validated on synthetic and real-world networks

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.