# Robust Graph Embedding with Noisy Link Weights

arXiv: https://arxiv.org/abs/1902.08440 · HTML: https://mlsanity.com/paper/1902.08440/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2019.

## Built on by (most cited first, 3 of 3)

- [DropMessage: Unifying Random Dropping for Graph Neural Networks](https://mlsanity.com/paper/2204.10037/) (2022)
- [Minimizing robust density power-based divergences for general parametric density models](https://mlsanity.com/paper/2307.05251/) (2023)
- [Identifiability of a statistical model with two latent vectors: Importance of the dimensionality relation and application to graph embedding](https://mlsanity.com/paper/2405.19760/) (2024)

## Abstract (arXiv)

We propose β-graph embedding for robustly learning feature vectors from data vectors and noisy link weights. A newly introduced empirical moment β-score reduces the influence of contamination and robustly measures the difference between the underlying correct expected weights of links and the specified generative model. The proposed method is computationally tractable; we employ a minibatch-based efficient stochastic algorithm and prove that this algorithm locally minimizes the empirical moment β-score. We conduct numerical experiments on synthetic and real-world datasets.