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 1–25 of 982 papers

TitleStatusHype
ROLAND: Graph Learning Framework for Dynamic GraphsCode3
Graph Domain Adaptation: Challenges, Progress and ProspectsCode2
A Survey of Pretraining on Graphs: Taxonomy, Methods, and ApplicationsCode2
Graph Neural Networks for Natural Language Processing: A SurveyCode2
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product NetworksCode2
NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation LearningCode2
Do Transformers Really Perform Bad for Graph Representation?Code2
Explanation-Preserving Augmentation for Semi-Supervised Graph Representation LearningCode2
Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender SystemsCode2
Recipe for a General, Powerful, Scalable Graph TransformerCode2
VideoSAGE: Video Summarization with Graph Representation LearningCode2
A Survey on Knowledge Graphs: Representation, Acquisition and ApplicationsCode2
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning NetworksCode2
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph EmbeddingsCode2
Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation LearningCode2
CogDL: A Comprehensive Library for Graph Deep LearningCode2
Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image AnalysisCode2
Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic NetworksCode2
Structure-Aware Transformer for Graph Representation LearningCode2
A Large-Scale Database for Graph Representation LearningCode1
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation LearningCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
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Benchmark Results

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