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 226–250 of 982 papers

TitleStatusHype
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
PAC-Bayesian Generalization Bounds for Knowledge Graph Representation LearningCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning—0
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis—0
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions—0
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
Uplifting Message Passing Neural Network with Graph Original Information—0
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning—0
DPGNN: Dual-Perception Graph Neural Network for Representation Learning—0
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks—0
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
FMGNN: Fused Manifold Graph Neural Network—0
Dual Graph Representation Learning—0
Dual Space Graph Contrastive Learning—0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning—0
Domain Adaptive Graph Classification—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers—0
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Benchmark Results

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