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 951–982 of 982 papers

TitleStatusHype
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
Tensor Graph Convolutional Network for Dynamic Graph Representation Learning—0
The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic—0
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges—0
Fundamental Limits of Deep Graph Convolutional Networks—0
The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version—0
The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field—0
Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs—0
TopER: Topological Embeddings in Graph Representation Learning—0
Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation—0
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation—0
Towards Fair Graph Representation Learning in Social Networks—0
Towards Feature Overcorrelation in Deeper Graph Neural Networks—0
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective—0
Towards Graph Representation Learning in Emergent Communication—0
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming—0
Towards Interpretable Molecular Graph Representation Learning—0
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Benchmark Results

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