# Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization

arXiv: https://arxiv.org/abs/2109.07016 · HTML: https://mlsanity.com/paper/2109.07016/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 14 Sep 2021.

## Builds on (most cited first, 16 of 16)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [struc2vec: Learning Node Representations from Structural Identity](https://mlsanity.com/paper/1704.03165/) (2017)
- [Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models](https://mlsanity.com/paper/2005.07959/) (2020)
- [Learning Structural Node Embeddings Via Diffusion Wavelets](https://mlsanity.com/paper/1710.10321/) (2017)
- [NetLSD: Hearing the Shape of a Graph](https://mlsanity.com/paper/1805.10712/) (2018)
- [Geometric Scattering for Graph Data Analysis](https://mlsanity.com/paper/1810.03068/) (2018)
- [Graph Isomorphism in Quasipolynomial Time](https://mlsanity.com/paper/1512.03547/) (2015)
- [A Simple Baseline Algorithm for Graph Classification](https://mlsanity.com/paper/1810.09155/) (2018)
- [Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path](https://mlsanity.com/paper/2106.09923/) (2021)
- [Embedding Node Structural Role Identity into Hyperbolic Space](https://mlsanity.com/paper/2011.01512/) (2020)

## Built on by (most cited first, 10 of 10)

- [Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures](https://mlsanity.com/paper/2407.09468/) (2024)
- [MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning](https://mlsanity.com/paper/2206.09280/) (2022)
- [Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networks](https://mlsanity.com/paper/2409.18235/) (2024)
- [Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach](https://mlsanity.com/paper/2301.09801/) (2023)
- [Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach](https://mlsanity.com/paper/2504.10733/) (2025)
- [Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?](https://mlsanity.com/paper/2208.08902/) (2022)
- [Graph-Level Embedding for Time-Evolving Graphs](https://mlsanity.com/paper/2306.01012/) (2023)
- [Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks](https://mlsanity.com/paper/2603.04209/) (2026)
- [EvoFormer: Learning Dynamic Graph-Level Representations with Structural and Temporal Bias Correction](https://mlsanity.com/paper/2508.15378/) (2025)
- [DynaSTy: A Framework for SpatioTemporal Node Attribute Prediction in Dynamic Graphs](https://mlsanity.com/paper/2601.05391/) (2026)

## Abstract (arXiv)

Recent years have seen a rise in the development of representational learning methods for graph data. Most of these methods, however, focus on node-level representation learning at various scales (e.g., microscopic, mesoscopic, and macroscopic node embedding). In comparison, methods for representation learning on whole graphs are currently relatively sparse. In this paper, we propose a novel unsupervised whole graph embedding method. Our method uses spectral graph wavelets to capture topological similarities on each k-hop sub-graph between nodes and uses them to learn embeddings for the whole graph. We evaluate our method against 12 well-known baselines on 4 real-world datasets and show that our method achieves the best performance across all experiments, outperforming the current state-of-the-art by a considerable margin.