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 191200 of 982 papers

TitleStatusHype
Graph Neural Networks with Adaptive ResidualCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic GraphsCode1
Graph Autoencoder for Graph Compression and Representation LearningCode1
A Large-Scale Database for Graph Representation LearningCode1
Deep Graph Contrastive Representation LearningCode1
Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discoveryCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
Deep Graph Representation Learning and Optimization for Influence MaximizationCode1
Graph Propagation Transformer for Graph Representation LearningCode1
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Benchmark Results

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