# DynamicGEM: A Library for Dynamic Graph Embedding Methods

arXiv: https://arxiv.org/abs/1811.10734 · HTML: https://mlsanity.com/paper/1811.10734/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2018.

## Built on by (most cited first, 8 of 8)

- [Towards Better Dynamic Graph Learning: New Architecture and Unified Library](https://mlsanity.com/paper/2303.13047/) (2023)
- [PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models](https://mlsanity.com/paper/2104.07788/) (2021)
- [A Survey on Embedding Dynamic Graphs](https://mlsanity.com/paper/2101.01229/) (2021)
- [DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs](https://mlsanity.com/paper/2406.12072/) (2024)
- [DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training](https://mlsanity.com/paper/2307.07649/) (2023)
- [A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers](https://mlsanity.com/paper/2412.11293/) (2024)
- [Curriculum Negative Mining For Temporal Networks](https://mlsanity.com/paper/2407.17070/) (2024)
- [LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning](https://mlsanity.com/paper/2311.16605/) (2023)

## Abstract (arXiv)

DynamicGEM is an open-source Python library for learning node representations of dynamic graphs. It consists of state-of-the-art algorithms for defining embeddings of nodes whose connections evolve over time. The library also contains the evaluation framework for four downstream tasks on the network: graph reconstruction, static and temporal link prediction, node classification, and temporal visualization. We have implemented various metrics to evaluate the state-of-the-art methods, and examples of evolving networks from various domains. We have easy-to-use functions to call and evaluate the methods and have extensive usage documentation. Furthermore, DynamicGEM provides a template to add new algorithms with ease to facilitate further research on the topic.