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Found 5 papers, 5 papers with code
Graph Entropy Minimization for Semi-supervised Node Classification
Node classifiers are required to comprehensively reduce prediction errors, training resources, and inference latency in the industry.
Unifying Label-inputted Graph Neural Networks with Deep Equilibrium Models
The unification facilitates the exchange between the two subdomains and inspires more studies.
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
Following the application of Deep Learning to graphic data, Graph Neural Networks (GNNs) have become the dominant method for Node Classification on graphs in recent years.
Ranked #1 on
Node Classification
on Amazon Photo
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
Nowadays, Graph Neural Networks (GNNs) following the Message Passing paradigm become the dominant way to learn on graphic data.
Ranked #1 on
Node Classification
on Cora Full
Memory-Associated Differential Learning
Conventional Supervised Learning approaches focus on the mapping from input features to output labels.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
