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 501–510 of 982 papers

TitleStatusHype
Wasserstein Hypergraph Neural Network—0
XLVIN: eXecuted Latent Value Iteration Nets—0
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning—0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning—0
A Benchmark on Directed Graph Representation Learning in Hardware Designs—0
A bi-diffusion based layer-wise sampling method for deep learning in large graphs—0
A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques—0
A Causal Disentangled Multi-Granularity Graph Classification Method—0
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks—0
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Benchmark Results

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