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 601–610 of 982 papers

TitleStatusHype
Learning Graph Search Heuristics—0
Self-supervised Graph Representation Learning for Black Market Account Detection—0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
Mitigating Relational Bias on Knowledge Graphs—0
End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning—0
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach—0
EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test—0
FairMILE: Towards an Efficient Framework for Fair Graph Representation LearningCode0
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Benchmark Results

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