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
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
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
Spectro-Riemannian Graph Neural Networks—0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space—0
Deep Modularity Networks with Diversity--Preserving Regularization—0
Graph Representation Learning with Diffusion Generative Models—0
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Benchmark Results

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