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
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph AnalysisCode1
Size-Invariant Graph Representations for Graph Classification ExtrapolationsCode1
Graph Autoencoder for Graph Compression and Representation LearningCode1
Towards a Unified Framework for Fair and Stable Graph Representation LearningCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
Graphonomy: Universal Image Parsing via Graph Reasoning and TransferCode1
Generating a Doppelganger Graph: Resembling but DistinctCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information MechanismCode1
Predicting Patient Outcomes with Graph Representation LearningCode1
xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future LinksCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Graph Mixture Density NetworksCode1
Node Similarity Preserving Graph Convolutional NetworksCode1
A Large-Scale Database for Graph Representation LearningCode1
A step towards neural genome assemblyCode1
Graph Neural Networks in Recommender Systems: A SurveyCode1
Handling Missing Data with Graph Representation LearningCode1
GripNet: Graph Information Propagation on Supergraph for Heterogeneous GraphsCode1
Graph Contrastive Learning with Adaptive AugmentationCode1
Personalised Meta-path Generation for Heterogeneous GNNsCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
Disentangle-based Continual Graph Representation LearningCode1
Reward Propagation Using Graph Convolutional NetworksCode1
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Benchmark Results

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