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 661–670 of 982 papers

TitleStatusHype
Spectral Augmentations for Graph Contrastive Learning—0
Graph Neural Networks With Lifting-based Adaptive Graph Wavelets—0
SpecTRA: Spectral Transformer for Graph Representation Learning—0
Spectro-Riemannian Graph Neural Networks—0
Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning—0
STERLING: Synergistic Representation Learning on Bipartite Graphs—0
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification—0
Structure and Features Fusion with Evidential Graph Convolutional Neural Network for Node Classification—0
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework—0
Studying and Improving Graph Neural Network-based Motif Estimation—0
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Benchmark Results

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