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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 81–90 of 982 papers

TitleStatusHype
A Generalization of ViT/MLP-Mixer to GraphsCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
A Representation Learning Framework for Property GraphsCode1
CCGL: Contrastive Cascade Graph LearningCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Show:102550
← PrevPage 9 of 99Next →

Benchmark Results

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