Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Graffe: Graph Representation Learning via Diffusion Probabilistic Models
Diffusion probabilistic models (DPMs), widely recognized for their potential to generate high-quality samples, tend to go unnoticed in representation learning. While recent progress has highlighted their potential for ca…
DenoisingGraph ClassificationGraph Representation LearningRepresentation LearningA Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction
The comorbidities of hypertension impose a heavy burden on patients and society. Early identification is necessary to prompt intervention, but it remains a challenging task. This study aims to address this challenge by c…
Graph LearningGraph Representation LearningRepresentation LearningPyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models
Existing biomedical benchmarks do not provide end-to-end infrastructure for training, evaluation, and inference of models that integrate multimodal biological data and a broad range of machine learning tasks in therapeut…
BenchmarkingGraph Representation LearningRepresentation LearningA Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities
Temporal interaction graphs (TIGs), defined by sequences of timestamped interaction events, have become ubiquitous in real-world applications due to their capability to model complex dynamic system behaviors. As a result…
Graph Representation LearningRepresentation LearningMulti-Granular Attention based Heterogeneous Hypergraph Neural Network
Heterogeneous graph neural networks (HeteGNNs) have demonstrated strong abilities to learn node representations by effectively extracting complex structural and semantic information in heterogeneous graphs. Most of the p…
Graph Representation LearningNode ClassificationNode ClusteringRepresentation LearningGRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype
Predicting genetic perturbations enables the identification of potentially crucial genes prior to wet-lab experiments, significantly improving overall experimental efficiency. Since genes are the foundation of cellular l…
Graph Neural NetworkGraph Representation LearningGraph structure learningLarge Language Model+1Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement
Surgical workflow recognition is vital for automating tasks, supporting decision-making, and training novice surgeons, ultimately improving patient safety and standardizing procedures. However, data corruption can lead t…
DisentanglementGraph Representation LearningRepresentation LearningABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning
Effective and efficient graph representation learning is essential for enabling critical downstream tasks, such as node classification, link prediction, and subgraph search. However, existing graph neural network (GNN) a…
Graph Neural NetworkGraph Representation LearningLink PredictionNeural Architecture Search+2OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning
Representation learning, a task of learning latent vectors to represent entities, is a key task in improving search and recommender systems in web applications. Various representation learning methods have been developed…
Contrastive LearningGraph Representation LearningRecommendation SystemsRepresentation LearningMitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-Attention
Graph Neural Networks (GNNs) update node representations through message passing, which is primarily based on the homophily principle, assuming that adjacent nodes share similar features. However, in real-world graphs wi…
FairnessGraph Representation LearningNode ClassificationNode Clustering+2Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection
RGB-Thermal Video Object Detection (RGBT VOD) can address the limitation of traditional RGB-based VOD in challenging lighting conditions, making it more practical and effective in many applications. However, similar to m…
Graph LearningGraph Representation Learningobject-detectionObject Detection+2GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector Quantization
Graph Transformers (GTs), which simultaneously integrate message-passing and self-attention mechanisms, have achieved promising empirical results in some graph prediction tasks. Although these approaches show the potenti…
Graph LearningGraph Representation LearningNode ClassificationQuantization+1Local Distance-Preserving Node Embeddings and Their Performance on Random Graphs
Learning node representations is a fundamental problem in graph machine learning. While existing embedding methods effectively preserve local similarity measures, they often fail to capture global functions like graph di…
Graph Representation LearningRepresentation LearningRobo-taxi Fleet Coordination at Scale via Reinforcement Learning
Fleets of robo-taxis offering on-demand transportation services, commonly known as Autonomous Mobility-on-Demand (AMoD) systems, hold significant promise for societal benefits, such as reducing pollution, energy consumpt…
Computational EfficiencyGraph Representation Learningreinforcement-learningReinforcement Learning+1Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems
This paper introduces a cutting-edge method for enhancing recommender systems through the integration of generative self-supervised learning (SSL) with a Residual Graph Transformer. Our approach emphasizes the importance…
Graph Representation LearningRecommendation SystemsRepresentation LearningSelf-Supervised LearningInductive Graph Representation Learning with Quantum Graph Neural Networks
Quantum Graph Neural Networks (QGNNs) present a promising approach for combining quantum computing with graph-structured data processing. While classical Graph Neural Networks (GNNs) are renowned for their scalability an…
Graph Representation LearningNode RegressionRepresentation LearningNode Embeddings via Neighbor Embeddings
Graph layouts and node embeddings are two distinct paradigms for non-parametric graph representation learning. In the former, nodes are embedded into 2D space for visualization purposes. In the latter, nodes are embedded…
Contrastive LearningGraph Representation LearningRepresentation LearningLGIN: Defining an Approximately Powerful Hyperbolic GNN
While graph neural networks (GNNs) operating in hyperbolic spaces have shown promise for modeling hierarchical and complex relational data, a critical limitation often overlooked is their potentially limited discriminati…
Graph LearningGraph Neural NetworkGraph Representation LearningRepresentation LearningMSNGO: multi-species protein function annotation based on 3D protein structure and network propagation
Motivation: In recent years, protein function prediction has broken through the bottleneck of sequence features, significantly improving prediction accuracy using high-precision protein structures predicted by AlphaFold2…
Graph Representation LearningPredictionProtein Function PredictionRepresentation LearningAugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
How can we accurately classify graphs? Graph classification is a pivotal task in data mining with applications in social network analysis, web analysis, drug discovery, molecular property prediction, etc. Graph neural ne…
DiversityDrug DiscoveryGraph ClassificationGraph Representation Learning+4