# MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning

arXiv: https://arxiv.org/abs/2406.02778 · HTML: https://mlsanity.com/paper/2406.02778/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization](https://mlsanity.com/paper/2012.04456/) (2020)
- [Shapley explainability on the data manifold](https://mlsanity.com/paper/2006.01272/) (2020)
- [Visualizing Large-scale and High-dimensional Data](https://mlsanity.com/paper/1602.00370/) (2016)
- [TriMap: Large-scale Dimensionality Reduction Using Triplets](https://mlsanity.com/paper/1910.00204/) (2019)
- [Geometric Scattering for Graph Data Analysis](https://mlsanity.com/paper/1810.03068/) (2018)
- [Stability of Graph Scattering Transforms](https://mlsanity.com/paper/1906.04784/) (2019)
- [On UMAP's true loss function](https://mlsanity.com/paper/2103.14608/) (2021)
- [Residual2Vec: Debiasing graph embedding with random graphs](https://mlsanity.com/paper/2110.07654/) (2021)
- [A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction](https://mlsanity.com/paper/2305.19043/) (2023)
- [Zero Shot Learning with the Isoperimetric Loss](https://mlsanity.com/paper/1903.06781/) (2019)
- [Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold](https://mlsanity.com/paper/1909.09218/) (2019)

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

- [MBExplainer: Multilevel bandit-based explanations for downstream models with augmented graph embeddings](https://mlsanity.com/paper/2411.00287/) (2024)

## Abstract (arXiv)

Deriving meaningful representations from complex, high-dimensional data in unsupervised settings is crucial across diverse machine learning applications. This paper introduces a framework for multi-scale graph network embedding based on spectral graph wavelets that employs a contrastive learning approach. We theoretically show that in Paley-Wiener spaces on combinatorial graphs, the spectral graph wavelets operator provides greater flexibility and control over smoothness compared to the Laplacian operator, motivating our approach. A key advantage of the proposed embedding is its ability to establish a correspondence between the embedding and input feature spaces, enabling the derivation of feature importance. We validate the effectiveness of our graph embedding framework on multiple public datasets across various downstream tasks, including clustering and unsupervised feature importance.