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 651–675 of 982 papers

TitleStatusHype
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning—0
SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning—0
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration—0
Simple yet Effective Gradient-Free Graph Convolutional Networks—0
Simple yet Effective Graph Distillation via Clustering—0
SMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs—0
Sparse Decomposition of Graph Neural Networks—0
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference—0
Spatio-Temporal Graph Representation Learning for Fraudster Group Detection—0
Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers—0
Spectral Augmentations for Graph Contrastive Learning—0
Graph Neural Networks With Lifting-based Adaptive Graph Wavelets—0
SpecTRA: Spectral Transformer for Graph Representation Learning—0
Spectro-Riemannian Graph Neural Networks—0
Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning—0
STERLING: Synergistic Representation Learning on Bipartite Graphs—0
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification—0
Structure and Features Fusion with Evidential Graph Convolutional Neural Network for Node Classification—0
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework—0
Studying and Improving Graph Neural Network-based Motif Estimation—0
Sub-GMN: The Neural Subgraph Matching Network Model—0
Supervised Graph Contrastive Learning for Gene Regulatory Network—0
Symmetry Breaking and Equivariant Neural Networks—0
Synergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN—0
Temporal Graph Representation Learning with Adaptive Augmentation Contrastive—0
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Benchmark Results

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