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 1–25 of 982 papers

TitleStatusHype
ROLAND: Graph Learning Framework for Dynamic GraphsCode3
Explanation-Preserving Augmentation for Semi-Supervised Graph Representation LearningCode2
VideoSAGE: Video Summarization with Graph Representation LearningCode2
Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image AnalysisCode2
Graph Domain Adaptation: Challenges, Progress and ProspectsCode2
Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic NetworksCode2
Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation LearningCode2
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product NetworksCode2
NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation LearningCode2
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning NetworksCode2
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph EmbeddingsCode2
Recipe for a General, Powerful, Scalable Graph TransformerCode2
A Survey of Pretraining on Graphs: Taxonomy, Methods, and ApplicationsCode2
Structure-Aware Transformer for Graph Representation LearningCode2
Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender SystemsCode2
Graph Neural Networks for Natural Language Processing: A SurveyCode2
Do Transformers Really Perform Bad for Graph Representation?Code2
CogDL: A Comprehensive Library for Graph Deep LearningCode2
A Survey on Knowledge Graphs: Representation, Acquisition and ApplicationsCode2
PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation modelsCode1
Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-AttentionCode1
Robo-taxi Fleet Coordination at Scale via Reinforcement LearningCode1
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a MeasurementCode1
Learning Efficient Positional Encodings with Graph Neural NetworksCode1
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
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Benchmark Results

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