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 201–225 of 982 papers

TitleStatusHype
Personalised Meta-path Generation for Heterogeneous GNNsCode1
Relational Deep Learning: Graph Representation Learning on Relational DatabasesCode1
GrokFormer: Graph Fourier Kolmogorov-Arnold TransformersCode1
Graph Contrastive Learning with Adaptive AugmentationCode1
Relphormer: Relational Graph Transformer for Knowledge Graph RepresentationsCode1
RepBin: Constraint-based Graph Representation Learning for Metagenomic BinningCode1
Heterogeneous Graph Representation Learning with Relation AwarenessCode1
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated LearningCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
GraphGT: Machine Learning Datasets for Graph Generation and TransformationCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learningCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Scaling Up Dynamic Graph Representation Learning via Spiking Neural NetworksCode1
Graph Mixture Density NetworksCode1
Graph Neural Networks in Recommender Systems: A SurveyCode1
Implicit Graphon Neural RepresentationCode1
SIGN: Scalable Inception Graph Neural NetworksCode1
Multi-hop Attention Graph Neural NetworkCode1
Graph Neural Networks with Adaptive ResidualCode1
Disentangle-based Continual Graph Representation LearningCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
LMSOC: An Approach for Socially Sensitive PretrainingCode1
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge FeaturesCode1
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question AnsweringCode1
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Benchmark Results

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