DynamicGEM: A Library for Dynamic Graph Embedding Methods
원문 초록
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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A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers
Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic Decomposition
Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction
Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
Structure-Preference Enabled Graph Embedding Generation under Differential Privacy
Multimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs
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