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 51–100 of 982 papers

TitleStatusHype
Data Augmentation on Graphs: A Technical SurveyCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
Graph Autoencoder for Graph Compression and Representation LearningCode1
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Deep Graph Representation Learning and Optimization for Influence MaximizationCode1
Evaluating Modules in Graph Contrastive LearningCode1
GraphGT: Machine Learning Datasets for Graph Generation and TransformationCode1
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
An Open Challenge for Inductive Link Prediction on Knowledge GraphsCode1
Generating a Doppelganger Graph: Resembling but DistinctCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on EchocardiogramsCode1
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand PredictionCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
Boosting Graph Structure Learning with Dummy NodesCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
A step towards neural genome assemblyCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
Adversarial Graph DisentanglementCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
CCGL: Contrastive Cascade Graph LearningCode1
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
A Representation Learning Framework for Property GraphsCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
A Gentle Introduction to Deep Learning for GraphsCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
Expander Graph PropagationCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
Fast Graph Representation Learning with PyTorch GeometricCode1
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation LearningCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
A Large-Scale Database for Graph Representation LearningCode1
Multi-hop Attention Graph Neural NetworkCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
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Benchmark Results

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