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 511–520 of 982 papers

TitleStatusHype
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
Accurate Text-Enhanced Knowledge Graph Representation Learning—0
A Class-Aware Representation Refinement Framework for Graph Classification—0
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers—0
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
A Comprehensive Survey on Deep Graph Representation Learning—0
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction—0
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing—0
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning—0
A Data-Driven Study of Commonsense Knowledge using the ConceptNet Knowledge Base—0
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Benchmark Results

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