DynamicGEM: A Library for Dynamic Graph Embedding Methods
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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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Nov 2018.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 3; 2024: 3.
8 papers.
- 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 157
- 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Modelscited by 118
- 2021A Survey on Embedding Dynamic Graphscited by 60
- 2024DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphscited by 27
- 2023DistTGL: Distributed Memory-Based Temporal Graph Neural Network Trainingcited by 12
- 2024A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformerscited by 3
- 2024Curriculum Negative Mining For Temporal Networkscited by 1
- 2023LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learningcited by 0
- 2024A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers15 Dec 2024
- 2024Curriculum Negative Mining For Temporal Networks24 Jul 2024
- 2024DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs17 Jun 2024
- 2023LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning28 Nov 2023
- 2023DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training14 Jul 2023
- 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Library23 Mar 2023
- 2021PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models15 Apr 2021
- 2021A Survey on Embedding Dynamic Graphs4 Jan 2021