# Dynamic Graph Echo State Networks

arXiv: https://arxiv.org/abs/2110.08565 · HTML: https://mlsanity.com/paper/2110.08565/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Representation Learning for Dynamic Graphs: A Survey](https://mlsanity.com/paper/1905.11485/) (2019)
- [A Gentle Introduction to Deep Learning for Graphs](https://mlsanity.com/paper/1912.12693/) (2019)
- [Fast and Deep Graph Neural Networks](https://mlsanity.com/paper/1911.08941/) (2019)
- [Temporal Graph Kernels for Classifying Dissemination Processes](https://mlsanity.com/paper/1911.05496/) (2019)

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

- [A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges](https://mlsanity.com/paper/2405.00476/) (2024)
- [A Primer on Temporal Graph Learning](https://mlsanity.com/paper/2401.03988/) (2024)
- [Using Connectome Features to Constrain Echo State Networks](https://mlsanity.com/paper/2206.02094/) (2022)
- [Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification](https://mlsanity.com/paper/2307.01237/) (2023)
- [Filtration Surfaces for Dynamic Graph Classification](https://mlsanity.com/paper/2309.03616/) (2023)
- [A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks](https://mlsanity.com/paper/2209.07332/) (2022)

## Abstract (arXiv)

Dynamic temporal graphs represent evolving relations between entities, e.g. interactions between social network users or infection spreading. We propose an extension of graph echo state networks for the efficient processing of dynamic temporal graphs, with a sufficient condition for their echo state property, and an experimental analysis of reservoir layout impact. Compared to temporal graph kernels that need to hold the entire history of vertex interactions, our model provides a vector encoding for the dynamic graph that is updated at each time-step without requiring training. Experiments show accuracy comparable to approximate temporal graph kernels on twelve dissemination process classification tasks.