DynamicGEM: A Library for Dynamic Graph Embedding Methods

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.

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10: This method uses deep auto-encoder to embed each node in the graph and aligns the embeddings at different time steps using a rotation matrix2018
11Dynamic TRIAD : This method utilizes the triadic closure process to generate a graph embedding that preserves structural and evolution patterns of the graph2018
12dyngraph2vecAE : This method models the interconnection of nodes within and across time using multiple fully connected layers2018

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