# On spectral partitioning of signed graphs

arXiv: https://arxiv.org/abs/1701.01394 · HTML: https://mlsanity.com/paper/1701.01394/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2017.

## Built on by (most cited first, 6 of 6)

- [Signed Graph Neural Networks: A Frequency Perspective](https://mlsanity.com/paper/2208.07323/) (2022)
- [Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation](https://mlsanity.com/paper/2602.15022/) (2026)
- [Federated Myopic Community Detection with One-shot Communication](https://mlsanity.com/paper/2106.07255/) (2021)
- [A Method for Handling Negative Similarities in Explainable Graph Spectral Clustering of Text Documents -- Extended Version](https://mlsanity.com/paper/2504.12360/) (2025)
- [A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy](https://mlsanity.com/paper/2102.08019/) (2021)
- [Signed graphs in data sciences via communicability geometry](https://mlsanity.com/paper/2403.07493/) (2024)

## Abstract (arXiv)

We argue that the standard graph Laplacian is preferable for spectral partitioning of signed graphs compared to the signed Laplacian. Simple examples demonstrate that partitioning based on signs of components of the leading eigenvectors of the signed Laplacian may be meaningless, in contrast to partitioning based on the Fiedler vector of the standard graph Laplacian for signed graphs. We observe that negative eigenvalues are beneficial for spectral partitioning of signed graphs, making the Fiedler vector easier to compute.