DynamicGEM: A Library for Dynamic Graph Embedding Methods
This is an incremental contribution, offering a tool to facilitate research in dynamic graph embedding for machine learning practitioners.
The authors introduced DynamicGEM, an open-source Python library for learning node representations in dynamic graphs, providing state-of-the-art algorithms and an evaluation framework for tasks like link prediction and node classification.
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.