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DynamicGEM: A Library for Dynamic Graph Embedding Methods

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arxiv 1811.10734 v1 pith:Y3FZFGPN submitted 2018-11-26 cs.LG cs.AIcs.SIstat.ML

classification cs.LGcs.AIcs.SIstat.ML
keywords dynamicgemlibrarymethodsalgorithmsdynamicevaluategraphnode
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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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  1. A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers

    cs.LG 2024-12 conditional novelty 4.0 of 10

    Mamba-based dynamic graph embedding models achieve comparable or better link prediction than transformer-based models on five benchmarks, with linear instead of quadratic scaling.

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