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 176–200 of 982 papers

TitleStatusHype
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation LearningCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-AttentionCode1
Modeling Two-Way Selection Preference for Person-Job FitCode1
Data Augmentation on Graphs: A Technical SurveyCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation LearningCode1
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
GCondenser: Benchmarking Graph CondensationCode1
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on GraphsCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Multi-view Tensor Graph Neural Networks Through Reinforced AggregationCode1
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)0.47—Unverified