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 211220 of 982 papers

TitleStatusHype
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Edge Representation Learning with HypergraphsCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph RepresentationsCode1
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic GraphsCode1
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
Retrieving Complex Tables with Multi-Granular Graph Representation LearningCode1
Reward Propagation Using Graph Convolutional NetworksCode1
Graph Propagation Transformer for Graph Representation LearningCode1
Multi-modal Graph Learning for Disease PredictionCode1
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Benchmark Results

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