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 251–260 of 982 papers

TitleStatusHype
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network—0
A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities—0
GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype—0
Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement—0
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation LearningCode0
OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning—0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection—0
Local Distance-Preserving Node Embeddings and Their Performance on Random GraphsCode0
Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems—0
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Benchmark Results

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