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

Showing 6170 of 982 papers

TitleStatusHype
Expander Graph PropagationCode1
An Open Challenge for Inductive Link Prediction on Knowledge GraphsCode1
Deep Graph Representation Learning and Optimization for Influence MaximizationCode1
Deep Graph Contrastive Representation LearningCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
A Representation Learning Framework for Property GraphsCode1
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Benchmark Results

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