REVIEW 2 cited by
DynamicGEM: A Library for Dynamic Graph Embedding Methods
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original 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.
Forward citations
Cited by 2 Pith papers
-
Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions
TNA, a stacked GCN-GRU model with variational sampling, predicts new edges in temporal graphs and outperforms baselines on three real datasets.
-
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.
Discussion (0). Continue with ORCID to comment.