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 351–375 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
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Benchmark Results

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