Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Joint Data Inpainting and Graph Learning via Unrolled Neural Networks
Given partial measurements of a time-varying graph signal, we propose an algorithm to simultaneously estimate both the underlying graph topology and the missing measurements. The proposed algorithm operates by training a…
Graph LearningA Benchmark for Fairness-Aware Graph Learning
Fairness-aware graph learning has gained increasing attention in recent years. Nevertheless, there lacks a comprehensive benchmark to evaluate and compare different fairness-aware graph learning methods, which blocks pra…
Computational EfficiencyFairnessGraph LearningWhen Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark
Graph mining has become crucial in fields such as social science, finance, and cybersecurity. Many large-scale real-world networks exhibit both heterogeneity, where multiple node and edge types exist in the graph, and he…
BenchmarkingGraph LearningGraph MiningModel Selection+1GeoMix: Towards Geometry-Aware Data Augmentation
Mixup has shown considerable success in mitigating the challenges posed by limited labeled data in image classification. By synthesizing samples through the interpolation of features and labels, Mixup effectively address…
Data AugmentationGraph Learningimage-classificationImage Classification+2SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation
Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider th…
Graph LearningImputationGraph Transformers: A Survey
Graph transformers are a recent advancement in machine learning, offering a new class of neural network models for graph-structured data. The synergy between transformers and graph learning demonstrates strong performanc…
DiversityGraph AttentionGraph LearningSurveyLearning a Mini-batch Graph Transformer via Two-stage Interaction Augmentation
Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustne…
Computational EfficiencyGraph LearningNode ClassificationSlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification
Existing WSI analysis methods lie on the consensus that histopathological characteristics of tumors are significant guidance for cancer diagnostics. Particularly, as the evolution of cancers is a continuous process, the …
graph constructionGraph LearningGraph Neural Networkimage-classification+4The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of Graph Neural Networks (GNNs) over tradi…
Graph LearningGraph Representation LearningRepresentation LearningLatent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model
Continuous-Time Dynamic Graph (CTDG) precisely models evolving real-world relationships, drawing heightened interest in dynamic graph learning across academia and industry. However, existing CTDG models encounter challen…
Data AugmentationGraph LearningSLRL: Structured Latent Representation Learning for Multi-view Clustering
In recent years, Multi-View Clustering (MVC) has attracted increasing attention for its potential to reduce the annotation burden associated with large datasets. The aim of MVC is to exploit the inherent consistency and …
ClusteringGraph LearningRepresentation LearningMEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning
We present the MEEG dataset, a multi-modal collection of music-induced electroencephalogram (EEG) recordings designed to capture emotional responses to various musical stimuli across different valence and arousal levels.…
Arousal EstimationEEGEEG Emotion RecognitionElectroencephalogram (EEG)+5GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation
Recent advances in learning multi-modal representation have witnessed the success in biomedical domains. While established techniques enable handling multi-modal information, the challenges are posed when extended to var…
BenchmarkingGraph EmbeddingGraph LearningHallucinationEdge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence
Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscellaneous intelligent services.Meanwhile, …
Edge-computingGraph LearningGraph Representation LearningMiscellaneous+1Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs
Graph classification benchmarks, vital for assessing and developing graph neural networks (GNNs), have recently been scrutinized, as simple methods like MLPs have demonstrated comparable performance. This leads to an imp…
BenchmarkingDataset GenerationGraph ClassificationGraph LearningConsistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations
Tripartite graph-based recommender systems markedly diverge from traditional models by recommending unique combinations such as user groups and item bundles. Despite their effectiveness, these systems exacerbate the long…
Contrastive LearningGraph LearningRecommendation SystemsFoundations and Frontiers of Graph Learning Theory
Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network architectures designed for learning gra…
Graph LearningLearning TheoryAutomated Knowledge Graph Learning in Industrial Processes
Industrial processes generate vast amounts of time series data, yet extracting meaningful relationships and insights remains challenging. This paper introduces a framework for automated knowledge graph learning from time…
Decision MakingGraph LearningKnowledge GraphsTime SeriesMulti-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties
Peptides are essential in biological processes and therapeutics. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models with Graph Neural Networks (GNNs) to pred…
Graph LearningProperty PredictionUnrolling Plug-and-Play Gradient Graph Laplacian Regularizer for Image Restoration
Generic deep learning (DL) networks for image restoration like denoising and interpolation lack mathematical interpretability, require voluminous training data to tune a large parameter set, and are fragile in the face o…
DenoisingGraph LearningImage Restoration