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

Showing 771780 of 982 papers

TitleStatusHype
Positional Encoding meets Persistent Homology on GraphsCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Spatial-Temporal Graph Representation Learning for Tactical Networks Future State PredictionCode0
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units DetectionCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
Graph Representation Learning Network via Adaptive SamplingCode0
Graph Representation Learning for Road Type ClassificationCode0
Towards Real-Time Temporal Graph LearningCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label ClassificationCode0
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Benchmark Results

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