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 491–500 of 982 papers

TitleStatusHype
Uplifting Message Passing Neural Network with Graph Original Information—0
Towards Real-Time Temporal Graph LearningCode0
Empowering Graph Representation Learning with Test-Time Graph TransformationCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
Expander Graph PropagationCode1
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
Understanding Substructures in Commonsense Relations in ConceptNet—0
Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation LearningCode1
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency ParsingCode0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
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Benchmark Results

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