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 651–660 of 982 papers

TitleStatusHype
Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender SystemsCode2
Do Transformers Really Perform Badly for Graph Representation?Code0
Graph Neural Networks with Adaptive ResidualCode1
Hierarchical Prototype Networks for Continual Graph Representation Learning—0
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
On the combination of graph data for assessing thin-file borrowers' creditworthiness—0
Multi-fidelity Stability for Graph Representation Learning—0
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming—0
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability—0
Structure and Features Fusion with Evidential Graph Convolutional Neural Network for Node Classification—0
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Benchmark Results

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