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 2130 of 982 papers

TitleStatusHype
Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-AttentionCode1
Robo-taxi Fleet Coordination at Scale via Reinforcement LearningCode1
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a MeasurementCode1
Learning Efficient Positional Encodings with Graph Neural NetworksCode1
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
Repository-Level Graph Representation Learning for Enhanced Security Patch DetectionCode1
GrokFormer: Graph Fourier Kolmogorov-Arnold TransformersCode1
When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation LearningCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Graph Representation Learning via Causal Diffusion for Out-of-Distribution RecommendationCode1
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Benchmark Results

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