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.
DynamicGEM: A Library for Dynamic Graph Embedding Methods
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
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.
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers
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.