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 81–90 of 982 papers

TitleStatusHype
Disentangle-based Continual Graph Representation LearningCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
A Representation Learning Framework for Property GraphsCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
A Gentle Introduction to Deep Learning for GraphsCode1
CCGL: Contrastive Cascade Graph LearningCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
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Benchmark Results

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