# Community Detection in Networks with Node Features

arXiv: https://arxiv.org/abs/1509.01173 · HTML: https://mlsanity.com/paper/1509.01173/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2015.

## Built on by (most cited first, 10 of 10)

- [Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities](https://mlsanity.com/paper/2107.06767/) (2021)
- [Latent Space Model for Higher-order Networks and Generalized Tensor Decomposition](https://mlsanity.com/paper/2106.16042/) (2021)
- [An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees](https://mlsanity.com/paper/2112.10467/) (2021)
- [Exact Community Recovery in Correlated Stochastic Block Models](https://mlsanity.com/paper/2203.15736/) (2022)
- [Bayesian community detection for networks with covariates](https://mlsanity.com/paper/2203.02090/) (2022)
- [Non-Convex Joint Community Detection and Group Synchronization via Generalized Power Method](https://mlsanity.com/paper/2112.14204/) (2021)
- [Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks](https://mlsanity.com/paper/2206.08465/) (2022)
- [Learning Joint and Individual Structure in Network Data with Covariates](https://mlsanity.com/paper/2406.08776/) (2024)
- [A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detection](https://mlsanity.com/paper/2112.04388/) (2021)
- [Community Detection for Contextual-LSBM: Theoretical Limitations of Misclassification Rate and Efficient Algorithms](https://mlsanity.com/paper/2501.11139/) (2025)

## Abstract (arXiv)

Many methods have been proposed for community detection in networks, but most of them do not take into account additional information on the nodes that is often available in practice. In this paper, we propose a new joint community detection criterion that uses both the network edge information and the node features to detect community structures. One advantage our method has over existing joint detection approaches is the flexibility of learning the impact of different features which may differ across communities. Another advantage is the flexibility of choosing the amount of influence the feature information has on communities. The method is asymptotically consistent under the block model with additional assumptions on the feature distributions, and performs well on simulated and real networks.