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 151–175 of 982 papers

TitleStatusHype
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
Adversarial Graph DisentanglementCode1
Does Invariant Graph Learning via Environment Augmentation Learn Invariance?Code1
Graph Autoencoder for Graph Compression and Representation LearningCode1
Graph Barlow Twins: A self-supervised representation learning framework for graphsCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
GRPE: Relative Positional Encoding for Graph TransformerCode1
A Fair Comparison of Graph Neural Networks for Graph ClassificationCode1
Graph Contrastive Learning with Adaptive AugmentationCode1
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
Edge Representation Learning with HypergraphsCode1
A Gentle Introduction to Deep Learning for GraphsCode1
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
Empowering Graph Representation Learning with Test-Time Graph TransformationCode1
Graph Representation Learning via Causal Diffusion for Out-of-Distribution RecommendationCode1
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Benchmark Results

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