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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 181–190 of 982 papers

TitleStatusHype
Large-Scale Representation Learning on Graphs via BootstrappingCode1
Graphonomy: Universal Image Parsing via Graph Reasoning and TransferCode1
Generating a Doppelganger Graph: Resembling but DistinctCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information MechanismCode1
Predicting Patient Outcomes with Graph Representation LearningCode1
xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future LinksCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Graph Mixture Density NetworksCode1
Node Similarity Preserving Graph Convolutional NetworksCode1
Show:102550
← PrevPage 19 of 99Next →

Benchmark Results

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