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 171–180 of 982 papers

TitleStatusHype
A Gentle Introduction to Deep Learning for GraphsCode1
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge FeaturesCode1
GrokFormer: Graph Fourier Kolmogorov-Arnold TransformersCode1
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph RepresentationCode1
Hierarchical Graph Representation Learning with Differentiable PoolingCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
Hierarchical Heterogeneous Graph Representation Learning for Short Text ClassificationCode1
Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation LearningCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Graph Representation Learning via Causal Diffusion for Out-of-Distribution RecommendationCode1
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Benchmark Results

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