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 511–520 of 982 papers

TitleStatusHype
RESTORE: Graph Embedding Assessment Through Reconstruction—0
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation LearningCode0
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition—0
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field—0
OCTAL: Graph Representation Learning for LTL Model Checking—0
Local Structure-aware Graph Contrastive Representation Learning—0
Biomedical Knowledge Graph Embeddings with Negative StatementsCode0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
Graph Contrastive Learning with Generative Adversarial Network—0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
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Benchmark Results

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