# Contrastive Deep Nonnegative Matrix Factorization for Community Detection

arXiv: https://arxiv.org/abs/2311.02357 · HTML: https://mlsanity.com/paper/2311.02357/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 4 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Deep Learning for Community Detection: Progress, Challenges and Opportunities](https://mlsanity.com/paper/2005.08225/) (2020)
- [VGAER: Graph Neural Network Reconstruction based Community Detection](https://mlsanity.com/paper/2201.04066/) (2022)
- [Reformulating Speaker Diarization as Community Detection With Emphasis On Topological Structure](https://mlsanity.com/paper/2204.12112/) (2022)
- [Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization](https://mlsanity.com/paper/1305.7169/) (2013)
- [On the Permanence of Vertices in Network Communities](https://mlsanity.com/paper/1406.2426/) (2014)
- [Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization](https://mlsanity.com/paper/2306.14530/) (2023)

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

- [Deep Cut-informed Graph Embedding and Clustering](https://mlsanity.com/paper/2503.06635/) (2025)
- [RecGOAT: Graph Optimal Adaptive Transport for LLM-Enhanced Multimodal Recommendation with Dual Semantic Alignment](https://mlsanity.com/paper/2602.00682/) (2026)
- [CueGCL: Cluster-aware Personalized Self-Training for Unsupervised Graph Contrastive Learning](https://mlsanity.com/paper/2311.11073/) (2023)
- [Hybrid Graph Embeddings and Louvain Algorithm for Unsupervised Community Detection](https://mlsanity.com/paper/2509.23411/) (2025)

## Abstract (arXiv)

Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability. However, the existing NMF-based methods have the following three problems: 1) they directly transform the original network into community membership space, so it is difficult for them to capture the hierarchical information; 2) they often only pay attention to the topology of the network and ignore its node attributes; 3) it is hard for them to learn the global structure information necessary for community detection. Therefore, we propose a new community detection algorithm, named Contrastive Deep Nonnegative Matrix Factorization (CDNMF). Firstly, we deepen NMF to strengthen its capacity for information extraction. Subsequently, inspired by contrastive learning, our algorithm creatively constructs network topology and node attributes as two contrasting views. Furthermore, we utilize a debiased negative sampling layer and learn node similarity at the community level, thereby enhancing the suitability of our model for community detection. We conduct experiments on three public real graph datasets and the proposed model has achieved better results than state-of-the-art methods. Code available at https://github.com/6lyc/CDNMF.git.