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
Graph Barlow Twins: A self-supervised representation learning framework for graphsCode1
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-based prediction of Protein-protein interactions with attributed signed graph embeddingCode1
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
Graph Trend Filtering Networks for RecommendationsCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Heterogeneous Graph Representation Learning with Relation AwarenessCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on EchocardiogramsCode1
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph AnalysisCode1
Edge Representation Learning with HypergraphsCode1
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender SystemsCode1
A Gentle Introduction to Deep Learning for GraphsCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
Implicit SVD for Graph Representation LearningCode1
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