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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 982 papers

TitleStatusHype
Deep Graph Representation Learning and Optimization for Influence MaximizationCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
Disentangle-based Continual Graph Representation LearningCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
Deep Graph Contrastive Representation LearningCode1
Multi-hop Attention Graph Neural NetworkCode1
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
Certifiably Robust Graph Contrastive LearningCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
A step towards neural genome assemblyCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
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
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
CCGL: Contrastive Cascade Graph LearningCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
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
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
Show:102550
← PrevPage 2 of 40Next →

Benchmark Results

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