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 76–100 of 982 papers

TitleStatusHype
Adversarial Graph DisentanglementCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
CCGL: Contrastive Cascade Graph LearningCode1
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
A Representation Learning Framework for Property GraphsCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
A Gentle Introduction to Deep Learning for GraphsCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
Expander Graph PropagationCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
Fast Graph Representation Learning with PyTorch GeometricCode1
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation LearningCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
A Large-Scale Database for Graph Representation LearningCode1
Multi-hop Attention Graph Neural NetworkCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
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Benchmark Results

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