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 271–280 of 982 papers

TitleStatusHype
A Class-Aware Representation Refinement Framework for Graph Classification—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype—0
Disentangled Generative Graph Representation Learning—0
Discriminative Graph Autoencoder—0
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning—0
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query—0
Directional diffusion models for graph representation learning—0
Directed Graph Embeddings in Pseudo-Riemannian Manifolds—0
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Benchmark Results

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