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
Graph-based prediction of Protein-protein interactions with attributed signed graph embeddingCode1
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node TasksCode1
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph RepresentationsCode1
Graph Contrastive Learning with Adaptive AugmentationCode1
Graph Contrastive Learning with Cohesive Subgraph AwarenessCode1
Graph External Attention Enhanced TransformerCode1
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution GeneralizationCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated LearningCode1
A Meta-Learning Approach for Graph Representation Learning in Multi-Task SettingsCode1
GraphGT: Machine Learning Datasets for Graph Generation and TransformationCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Handling Missing Data with Graph Representation LearningCode1
Graphonomy: Universal Image Parsing via Graph Reasoning and TransferCode1
Sign and Basis Invariant Networks for Spectral Graph Representation LearningCode1
Graph Mixture Density NetworksCode1
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data AugmentationCode1
Multi-hop Attention Graph Neural NetworkCode1
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
Disentangle-based Continual Graph Representation LearningCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
Learning Semantic-Specific Graph Representation for Multi-Label Image RecognitionCode1
Graph Trend Filtering Networks for RecommendationsCode1
PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation modelsCode1
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Benchmark Results

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