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 151–200 of 982 papers

TitleStatusHype
Disentangle-based Continual Graph Representation LearningCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
Adversarial Graph DisentanglementCode1
GraphNorm: A Principled Approach to Accelerating Graph Neural Network TrainingCode1
Generative Subgraph Contrast for Self-Supervised Graph Representation LearningCode1
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
Hierarchical Heterogeneous Graph Representation Learning for Short Text ClassificationCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Generating a Doppelganger Graph: Resembling but DistinctCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
How Expressive are Transformers in Spectral Domain for Graphs?Code1
A Generalization of ViT/MLP-Mixer to GraphsCode1
Empowering Graph Representation Learning with Test-Time Graph TransformationCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
K-Core based Temporal Graph Convolutional Network 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
Evaluating Modules in Graph Contrastive LearningCode1
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic GraphsCode1
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)Code1
A Gentle Introduction to Deep Learning for GraphsCode1
Expander Graph PropagationCode1
Graph Mixture Density NetworksCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
Graph Representation Learning via Causal Diffusion for Out-of-Distribution RecommendationCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on GraphsCode1
M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender SystemsCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
MAGNET: Multi-Label Text Classification using Attention-based Graph Neural NetworkCode1
Fast Graph Representation Learning with PyTorch GeometricCode1
MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learningCode1
Data Augmentation on Graphs: A Technical SurveyCode1
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation LearningCode1
Motif-based Graph Representation Learning with Application to Chemical MoleculesCode1
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
GCondenser: Benchmarking Graph CondensationCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Multi-view Tensor Graph Neural Networks Through Reinforced AggregationCode1
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across CitiesCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Geometry-Complete Perceptron Networks for 3D Molecular GraphsCode1
Information Obfuscation of Graph Neural NetworksCode1
A Large-Scale Database for Graph Representation LearningCode1
Deep Graph Contrastive Representation LearningCode1
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
Deep Graph Mapper: Seeing Graphs through the Neural LensCode1
Deep Graph Representation Learning and Optimization for Influence MaximizationCode1
Graphonomy: Universal Image Parsing via Graph Reasoning and TransferCode1
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Benchmark Results

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