# Hidden Community Detection on Two-layer Stochastic Models: a Theoretical Perspective

arXiv: https://arxiv.org/abs/2001.05919 · HTML: https://mlsanity.com/paper/2001.05919/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Structure Amplification on Multi-layer Stochastic Block Models](https://mlsanity.com/paper/2108.00127/) (2021)
- [Uncovering the Local Hidden Community Structure in Social Networks](https://mlsanity.com/paper/2112.04100/) (2021)

## Abstract (arXiv)

Hidden community is a new graph-theoretical concept recently proposed [4], in which the authors also propose a meta-approach called HICODE (Hidden Community Detection) for detecting hidden communities. HICODE is demonstrated through experiments that it is able to uncover previously overshadowed weak layers and uncover both weak and strong layers at a higher accuracy. However, the authors provide no theoretical guarantee for the performance. In this work, we focus on the theoretical analysis of HICODE on synthetic two-layer networks, where layers are independent of each other and each layer is generated by stochastic block model. We bridge their gap through two-layer stochastic block model networks in the following aspects: 1) we show that partitions that locally optimize modularity correspond to grounded layers, indicating modularity-optimizing algorithms can detect strong layers; 2) we prove that when reducing found layers, HICODE increases absolute modularities of all unreduced layers, showing its layer reduction step makes weak layers more detectable. Our work builds a solid theoretical base for HICODE, demonstrating that it is promising in uncovering both weak and strong layers of communities in two-layer networks.