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 41–50 of 982 papers

TitleStatusHype
Multi-View Node Pruning for Accurate Graph Representation—0
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis—0
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a MeasurementCode1
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
Diffusion Model Agnostic Social Influence Maximization in Hyperbolic Space—0
Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin—0
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive PretrainingCode0
Graph Contrastive Learning for Connectome ClassificationCode0
Learning Efficient Positional Encodings with Graph Neural NetworksCode1
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
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Benchmark Results

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