# Network Enhancement: a general method to denoise weighted biological networks

arXiv: https://arxiv.org/abs/1805.03327 · HTML: https://mlsanity.com/paper/1805.03327/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 15 of 15)

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- [Semi-supervised Domain Adaptation in Graph Transfer Learning](https://mlsanity.com/paper/2309.10773/) (2023)
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- [Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport](https://mlsanity.com/paper/2301.12721/) (2023)
- [Mask-GVAE: Blind Denoising Graphs via Partition](https://mlsanity.com/paper/2102.04228/) (2021)
- [FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification](https://mlsanity.com/paper/2210.13815/) (2022)
- [Hypothesis Testing for Equality of Latent Positions in Random Graphs](https://mlsanity.com/paper/2105.10838/) (2021)
- [Popularity Adjusted Block Models are Generalized Random Dot Product Graphs](https://mlsanity.com/paper/2109.04010/) (2021)
- [Network Embedding via Deep Prediction Model](https://mlsanity.com/paper/2104.13323/) (2021)
- [Strongly Consistent Community Detection in Popularity Adjusted Block Models](https://mlsanity.com/paper/2506.07224/) (2025)
- [Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning](https://mlsanity.com/paper/2506.10628/) (2025)
- [Network Embedding Using Sparse Approximations of Random Walks](https://mlsanity.com/paper/2308.13663/) (2023)
- [Attacking and Securing Community Detection: A Game-Theoretic Framework](https://mlsanity.com/paper/2512.11359/) (2025)
- [Network Denoising Revisited: A Ricci-Flow-Inspired Graph Diffusion Method](https://mlsanity.com/paper/2608.16923/) (2026)

## Abstract (arXiv)

Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. However, biological networks are noisy due to the limitations of measurement technology and inherent natural variation, which can hamper discovery of network patterns and dynamics. We propose Network Enhancement (NE), a method for improving the signal-to-noise ratio of undirected, weighted networks. NE uses a doubly stochastic matrix operator that induces sparsity and provides a closed-form solution that increases spectral eigengap of the input network. As a result, NE removes weak edges, enhances real connections, and leads to better downstream performance. Experiments show that NE improves gene function prediction by denoising tissue-specific interaction networks, alleviates interpretation of noisy Hi-C contact maps from the human genome, and boosts fine-grained identification accuracy of species. Our results indicate that NE is widely applicable for denoising biological networks.