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 26–50 of 982 papers

TitleStatusHype
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
Multi-hop Attention Graph Neural NetworkCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Certifiably Robust Graph Contrastive LearningCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
A Gentle Introduction to Deep Learning for GraphsCode1
CCGL: Contrastive Cascade Graph LearningCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
A Large-Scale Database for Graph Representation LearningCode1
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation LearningCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
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Benchmark Results

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