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 126–150 of 982 papers

TitleStatusHype
MAGNET: Multi-Label Text Classification using Attention-based Graph Neural NetworkCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on EchocardiogramsCode1
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Does Graph Distillation See Like Vision Dataset Counterpart?Code1
DropMessage: Unifying Random Dropping for Graph Neural NetworksCode1
CCGL: Contrastive Cascade Graph LearningCode1
A Representation Learning Framework for Property GraphsCode1
Multi-hop Attention Graph Neural NetworkCode1
Edge Representation Learning with HypergraphsCode1
Certifiably Robust Graph Contrastive LearningCode1
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Disentangle-based Continual Graph Representation LearningCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
A step towards neural genome assemblyCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
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Benchmark Results

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