SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

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Showing 521–530 of 982 papers

TitleStatusHype
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?Code0
Representation Learning on Graphs to Identifying Circular Trading in Goods and Services Tax—0
Scaling Up Dynamic Graph Representation Learning via Spiking Neural NetworksCode1
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query—0
ARIEL: Adversarial Graph Contrastive LearningCode0
MM-GNN: Mix-Moment Graph Neural Network towards Modeling Neighborhood Feature DistributionCode0
ROLAND: Graph Learning Framework for Dynamic GraphsCode3
Motif-based Graph Representation Learning with Application to Chemical MoleculesCode1
Towards Graph Representation Learning Based Surgical Workflow AnticipationCode0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified