DynamicGEM: A Library for Dynamic Graph Embedding Methods
Published in
arXiv.org(2018)External Links:
TL
TL;DR
This work has implemented various metrics to evaluate the state-of-the-art methods, and examples of evolving networks from various domains, and provides a template to add new algorithms with ease to facilitate further research on the topic.
Abstract
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.
References16 items
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dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning
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GEM: A Python package for graph embedding methods
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DynGEM: Deep Embedding Method for Dynamic Graphs
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Dynamic Network Embedding by Modeling Triadic Closure Process
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