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 26–50 of 982 papers

TitleStatusHype
Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement—0
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation LearningCode0
OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning—0
Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-AttentionCode1
Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection—0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
Local Distance-Preserving Node Embeddings and Their Performance on Random GraphsCode0
Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems—0
Robo-taxi Fleet Coordination at Scale via Reinforcement LearningCode1
LGIN: Defining an Approximately Powerful Hyperbolic GNNCode0
Node Embeddings via Neighbor Embeddings—0
Inductive Graph Representation Learning with Quantum Graph Neural Networks—0
MSNGO: multi-species protein function annotation based on 3D protein structure and network propagationCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Multi-View Node Pruning for Accurate Graph Representation—0
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis—0
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a MeasurementCode1
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
Diffusion Model Agnostic Social Influence Maximization in Hyperbolic Space—0
Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin—0
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive PretrainingCode0
Graph Contrastive Learning for Connectome ClassificationCode0
Learning Efficient Positional Encodings with Graph Neural NetworksCode1
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
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Benchmark Results

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