# Node Classification for Signed Social Networks Using Diffuse Interface Methods

arXiv: https://arxiv.org/abs/1809.06432 · HTML: https://mlsanity.com/paper/1809.06432/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Exact Community Recovery over Signed Graphs](https://mlsanity.com/paper/2202.12255/) (2022)

## Abstract (arXiv)

Signed networks contain both positive and negative kinds of interactions like friendship and enmity. The task of node classification in non-signed graphs has proven to be beneficial in many real world applications, yet extensions to signed networks remain largely unexplored. In this paper we introduce the first analysis of node classification in signed social networks via diffuse interface methods based on the Ginzburg-Landau functional together with different extensions of the graph Laplacian to signed networks. We show that blending the information from both positive and negative interactions leads to performance improvement in real signed social networks, consistently outperforming the current state of the art.