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
Robust Graph Representation Learning for Local Corruption RecoveryCode0
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
Structure-Aware Transformer for Graph Representation LearningCode2
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data AugmentationCode1
A Variational Edge Partition Model for Supervised Graph Representation LearningCode0
Urban Region Profiling via A Multi-Graph Representation Learning Framework—0
Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft—0
Molecular Representation Learning via Heterogeneous Motif Graph Neural NetworksCode1
When Do Flat Minima Optimizers Work?Code1
Memory-based Message Passing: Decoupling the Message for Propogation from DiscriminationCode0
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Benchmark Results

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