REVIEW 1 cited by
Benchmarking Graph Neural Networks on Link Prediction
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
read the original abstract
In this paper, we benchmark several existing graph neural network (GNN) models on different datasets for link predictions. In particular, the graph convolutional network (GCN), GraphSAGE, graph attention network (GAT) as well as variational graph auto-encoder (VGAE) are implemented dedicated to link prediction tasks, in-depth analysis are performed, and results from several different papers are replicated, also a more fair and systematic comparison are provided. Our experiments show these GNN architectures perform similarly on various benchmarks for link prediction tasks.
Forward citations
Cited by 1 Pith paper
-
The Missing Link: Joint Legal Citation Prediction using Heterogeneous Graph Enrichment
A graph neural network that enriches legal citation graphs with categorical metadata nodes predicts case and law citations more accurately than prior GNN baselines, and joint training boosts case citation prediction.
Discussion (0). Continue with ORCID to comment.