# Uncovering the Local Hidden Community Structure in Social Networks

arXiv: https://arxiv.org/abs/2112.04100 · HTML: https://mlsanity.com/paper/2112.04100/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 57 references that are in the map.
- First posted on arXiv 8 Dec 2021.

## Builds on (most cited first, 18 of 18)

- [MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](https://mlsanity.com/paper/2002.01680/) (2020)
- [Structural Deep Clustering Network](https://mlsanity.com/paper/2002.01633/) (2020)
- [Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning](https://mlsanity.com/paper/2105.09111/) (2021)
- [Attributed Graph Clustering via Adaptive Graph Convolution](https://mlsanity.com/paper/1906.01210/) (2019)
- [Adaptive Graph Encoder for Attributed Graph Embedding](https://mlsanity.com/paper/2007.01594/) (2020)
- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [A Comprehensive Survey on Community Detection with Deep Learning](https://mlsanity.com/paper/2105.12584/) (2021)
- [A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2101.01669/) (2021)
- [Heat kernel based community detection](https://mlsanity.com/paper/1403.3148/) (2014)
- [Community detection in node-attributed social networks: a survey](https://mlsanity.com/paper/1912.09816/) (2019)
- [CommunityGAN: Community Detection with Generative Adversarial Nets](https://mlsanity.com/paper/1901.06631/) (2019)
- [Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach](https://mlsanity.com/paper/1509.07715/) (2015)
- [Detecting Overlapping Communities from Local Spectral Subspaces](https://mlsanity.com/paper/1509.08065/) (2015)
- [Hidden Community Detection in Social Networks](https://mlsanity.com/paper/1702.07462/) (2017)
- [Revealing Multiple Layers of Hidden Community Structure in Networks](https://mlsanity.com/paper/1501.05700/) (2015)
- [Hidden Community Detection on Two-layer Stochastic Models: a Theoretical Perspective](https://mlsanity.com/paper/2001.05919/) (2020)
- [Markov random walk under constraint for discovering overlapping communities in complex networks](https://mlsanity.com/paper/1303.5675/) (2013)
- [Sparse Nonnegative Matrix Factorization for Multiple Local Community Detection](https://mlsanity.com/paper/2001.06951/) (2020)

## Abstract (arXiv)

Hidden community is a useful concept proposed recently for social network analysis. To handle the rapid growth of network scale, in this work, we explore the detection of hidden communities from the local perspective, and propose a new method that detects and boosts each layer iteratively on a subgraph sampled from the original network. We first expand the seed set from a single seed node based on our modified local spectral method and detect an initial dominant local community. Then we temporarily remove the members of this community as well as their connections to other nodes, and detect all the neighborhood communities in the remaining subgraph, including some "broken communities" that only contain a fraction of members in the original network. The local community and neighborhood communities form a dominant layer, and by reducing the edge weights inside these communities, we weaken this layer's structure to reveal the hidden layers. Eventually, we repeat the whole process and all communities containing the seed node can be detected and boosted iteratively. We theoretically show that our method can avoid some situations that a broken community and the local community are regarded as one community in the subgraph, leading to the inaccuracy on detection which can be caused by global hidden community detection methods. Extensive experiments show that our method could significantly outperform the state-of-the-art baselines designed for either global hidden community detection or multiple local community detection.