Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Time-Varying Graph Learning for Data with Heavy-Tailed Distribution
Graph models provide efficient tools to capture the underlying structure of data defined over networks. Many real-world network topologies are subject to change over time. Learning to model the dynamic interactions betwe…
Graph LearningMissing ValuesConservation-informed Graph Learning for Spatiotemporal Dynamics Prediction
Data-centric methods have shown great potential in understanding and predicting spatiotemporal dynamics, enabling better design and control of the object system. However, deep learning models often lack interpretability,…
Graph LearningPredictionOvercoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations
Class imbalance is pervasive in real-world graph datasets, where the majority of annotated nodes belong to a small set of classes (majority classes), leaving many other classes (minority classes) with only a handful of l…
Graph LearningGraph Neural NetworkNode ClassificationPseudo LabelCausal Discovery on Dependent Binary Data
The assumption of independence between observations (units) in a dataset is prevalent across various methodologies for learning causal graphical models. However, this assumption often finds itself in conflict with real-w…
Causal DiscoveryGraph LearningVirtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
Federated Graph Learning (FGL) enables multiple clients to jointly train powerful graph learning models, e.g., Graph Neural Networks (GNNs), without sharing their local graph data for graph-related downstream tasks, such…
Graph LearningGraph Property PredictionProperty PredictionERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters
Approximation-based spectral graph neural networks, which construct graph filters with function approximation, have shown substantial performance in graph learning tasks. Despite their great success, existing works prima…
Graph LearningGraph Neural NetworkLarge Language Models Meet Graph Neural Networks: A Perspective of Graph Mining
Graph mining is an important area in data mining and machine learning that involves extracting valuable information from graph-structured data. In recent years, significant progress has been made in this field through th…
Community DetectionGraph LearningGraph MiningLink Prediction+1Enhancing Federated Graph Learning via Adaptive Fusion of Structural and Node Characteristics
Federated Graph Learning (FGL) has demonstrated the advantage of training a global Graph Neural Network (GNN) model across distributed clients using their local graph data. Unlike Euclidean data (\eg, images), graph data…
Graph LearningGraph Neural NetworkExploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning
Graph Mamba, a powerful graph embedding technique, has emerged as a cornerstone in various domains, including bioinformatics, social networks, and recommendation systems. This survey represents the first comprehensive st…
Graph EmbeddingGraph LearningMambaRecommendation Systems+2AutoSculpt: A Pattern-based Model Auto-pruning Framework Using Reinforcement Learning and Graph Learning
As deep neural networks (DNNs) are increasingly deployed on edge devices, optimizing models for constrained computational resources is critical. Existing auto-pruning methods face challenges due to the diversity of DNN m…
Deep Reinforcement LearningGraph LearningFedGIG: Graph Inversion from Gradient in Federated Learning
Recent studies have shown that Federated learning (FL) is vulnerable to Gradient Inversion Attacks (GIA), which can recover private training data from shared gradients. However, existing methods are designed for dense, c…
Federated LearningGraph LearningNoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation Learning
Real-world graph data environments intrinsically exist noise (e.g., link and structure errors) that inevitably disturb the effectiveness of graph representation and downstream learning tasks. For homogeneous graphs, the …
Graph LearningGraph Neural NetworkGraph Representation LearningRepresentation LearningMulti-view Fuzzy Graph Attention Networks for Enhanced Graph Learning
Fuzzy Graph Attention Network (FGAT), which combines Fuzzy Rough Sets and Graph Attention Networks, has shown promise in tasks requiring robust graph-based learning. However, existing models struggle to effectively captu…
Graph AttentionGraph ClassificationGraph LearningGraph Learning-based Regional Heavy Rainfall Prediction Using Low-Cost Rain Gauges
Accurate and timely prediction of heavy rainfall events is crucial for effective flood risk management and disaster preparedness. By monitoring, analysing, and evaluating rainfall data at a local level, it is not only po…
Graph LearningPredictionTHeGCN: Temporal Heterophilic Graph Convolutional Network
Graph Neural Networks (GNNs) have exhibited remarkable efficacy in diverse graph learning tasks, particularly on static homophilic graphs. Recent attention has pivoted towards more intricate structures, encompassing (1) …
Graph LearningGraphSeqLM: A Unified Graph Language Framework for Omic Graph Learning
The integration of multi-omic data is pivotal for understanding complex diseases, but its high dimensionality and noise present significant challenges. Graph Neural Networks (GNNs) offer a robust framework for analyzing …
Data IntegrationGraph LearningLanguage ModelingLanguage ModellingFedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks
Federated training methods have gained popularity for graph learning with applications including friendship graphs of social media sites and customer-merchant interaction graphs of huge online marketplaces. However, priv…
Graph AttentionGraph LearningNode ClassificationPrivacy PreservingSpectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation
Incorporating multi-modal features as side information has recently become a trend in recommender systems. To elucidate user-item preferences, recent studies focus on fusing modalities via concatenation, element-wise sum…
Graph LearningMultimodal RecommendationRecommendation SystemsCommunication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
Federated graph learning (FGL) has gained significant attention for enabling heterogeneous clients to process their private graph data locally while interacting with a centralized server, thus maintaining privacy. Howeve…
Graph LearningBenchmarking and Improving Large Vision-Language Models for Fundamental Visual Graph Understanding and Reasoning
Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across diverse tasks. Despite great success, recent studies show that LVLMs encounter substantial limitations when engaging with visual graphs…
BenchmarkingGraph LearningSelf-Supervised Learning