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 201–225 of 982 papers

TitleStatusHype
Graph Representation Learning via Aggregation EnhancementCode1
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
PC-Conv: Unifying Homophily and Heterophily with Two-fold FilteringCode1
Edge Representation Learning with HypergraphsCode1
Predicting Patient Outcomes with Graph Representation LearningCode1
PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation modelsCode1
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question AnsweringCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Expander Graph PropagationCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
Relational Deep Learning: Graph Representation Learning on Relational DatabasesCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph RepresentationsCode1
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group DiscriminationCode1
Reward Propagation Using Graph Convolutional NetworksCode1
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale GraphsCode1
Multi-hop Attention Graph Neural NetworkCode1
Scaling Up Dynamic Graph Representation Learning via Spiking Neural NetworksCode1
Disentangle-based Continual Graph Representation LearningCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
Graph Propagation Transformer for Graph Representation LearningCode1
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
Multi-modal Graph Learning for Disease PredictionCode1
Show:102550
← PrevPage 9 of 40Next →

Benchmark Results

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