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

Showing 201210 of 982 papers

TitleStatusHype
Node Similarity Preserving Graph Convolutional NetworksCode1
Graph Representation Learning via Aggregation EnhancementCode1
Empowering Graph Representation Learning with Test-Time Graph TransformationCode1
PAGE: Prototype-Based Model-Level Explanations for Graph Neural NetworksCode1
Periodic Graph Transformers for Crystal Material Property PredictionCode1
Predicting Patient Outcomes with Graph Representation LearningCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on EchocardiogramsCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
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Benchmark Results

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