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 351–400 of 982 papers

TitleStatusHype
Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation LearningCode1
ENGAGE: Explanation Guided Data Augmentation for Graph Representation LearningCode0
Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective—0
Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discoveryCode1
Directional diffusion models for graph representation learning—0
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method—0
Mixed-Curvature Transformers for Graph Representation Learning papersreview—0
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions—0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Self-supervised Learning and Graph Classification under Heterophily—0
CARL-G: Clustering-Accelerated Representation Learning on Graphs—0
Virtual Node Tuning for Few-shot Node Classification—0
Point-Voxel Absorbing Graph Representation Learning for Event Stream based RecognitionCode0
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification—0
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation—0
Graph-Level Embedding for Time-Evolving Graphs—0
Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation LearningCode2
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node ClusteringCode0
Union Subgraph Neural NetworksCode0
Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs—0
Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention—0
Graph Propagation Transformer for Graph Representation LearningCode1
Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product NetworksCode2
Neural Oscillators are Universal—0
Semantic Random Walk for Graph Representation Learning in Attributed Graphs—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
Towards Better Graph Representation Learning with Parameterized Decomposition & FilteringCode1
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning QuestionCode0
Hierarchical Transformer for Scalable Graph Learning—0
Deep Graph Representation Learning and Optimization for Influence MaximizationCode1
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation LearningCode2
Connector 0.5: A unified framework for graph representation learningCode0
Capturing Fine-grained Semantics in Contrastive Graph Representation Learning—0
What Do GNNs Actually Learn? Towards Understanding their RepresentationsCode0
Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer—0
Stochastic Subgraph Neighborhood Pooling for Subgraph ClassificationCode0
Multi-View Graph Representation Learning Beyond HomophilyCode0
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks—0
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
A Comprehensive Survey on Deep Graph Representation Learning—0
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
Class-Imbalanced Learning on Graphs: A SurveyCode1
Graph Representation Learning for Interactive Biomolecule Systems—0
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment—0
FMGNN: Fused Manifold Graph Neural Network—0
Multi-view Tensor Graph Neural Networks Through Reinforced AggregationCode1
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Benchmark Results

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