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 1–50 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
Repository-Level Graph Representation Learning for Enhanced Security Patch DetectionCode1
GrokFormer: Graph Fourier Kolmogorov-Arnold TransformersCode1
When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation LearningCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Graph Representation Learning via Causal Diffusion for Out-of-Distribution RecommendationCode1
Learning Long Range Dependencies on Graphs via Random WalksCode1
Graph External Attention Enhanced TransformerCode1
Learning-Based Link Anomaly Detection in Continuous-Time Dynamic GraphsCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
GCondenser: Benchmarking Graph CondensationCode1
PAC-Bayesian Generalization Bounds for Knowledge Graph Representation LearningCode1
Temporal Graph ODEs for Irregularly-Sampled Time SeriesCode1
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FRONDCode1
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph RepresentationCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation LearningCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt LearningCode1
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive LearningCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
PC-Conv: Unifying Homophily and Heterophily with Two-fold FilteringCode1
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution GeneralizationCode1
Relational Deep Learning: Graph Representation Learning on Relational DatabasesCode1
Recurrent Distance Filtering for Graph Representation LearningCode1
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Benchmark Results

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