# Community detection with nodal information

arXiv: https://arxiv.org/abs/1610.09735 · HTML: https://mlsanity.com/paper/1610.09735/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2016.

## Built on by (most cited first, 4 of 4)

- [An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees](https://mlsanity.com/paper/2112.10467/) (2021)
- [Bayesian community detection for networks with covariates](https://mlsanity.com/paper/2203.02090/) (2022)
- [Community detection in censored hypergraph](https://mlsanity.com/paper/2111.03179/) (2021)
- [Perfect Spectral Clustering with Discrete Covariates](https://mlsanity.com/paper/2205.08047/) (2022)

## Abstract (arXiv)

Community detection is one of the fundamental problems in the study of network data. Most existing community detection approaches only consider edge information as inputs, and the output could be suboptimal when nodal information is available. In such cases, it is desirable to leverage nodal information for the improvement of community detection accuracy. Towards this goal, we propose a flexible network model incorporating nodal information, and develop likelihood-based inference methods. For the proposed methods, we establish favorable asymptotic properties as well as efficient algorithms for computation. Numerical experiments show the effectiveness of our methods in utilizing nodal information across a variety of simulated and real network data sets.