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 101110 of 982 papers

TitleStatusHype
Empowering Graph Representation Learning with Test-Time Graph TransformationCode1
Geodesic Graph Neural Network for Efficient Graph Representation LearningCode1
Expander Graph PropagationCode1
Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation LearningCode1
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
Periodic Graph Transformers for Crystal Material Property PredictionCode1
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text ClassificationCode1
Structure-Preserving Graph Representation LearningCode1
Relational Self-Supervised Learning on GraphsCode1
Modeling Two-Way Selection Preference for Person-Job FitCode1
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Benchmark Results

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