Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Graph Learning for Planning: The Story Thus Far and Open Challenges
Graph learning is naturally well suited for use in planning due to its ability to exploit relational structures exhibited in planning domains and to take as input planning instances with arbitrary number of objects. In t…
Graph LearningHybridMQA: Exploring Geometry-Texture Interactions for Colored Mesh Quality Assessment
Mesh quality assessment (MQA) models play a critical role in the design, optimization, and evaluation of mesh operation systems in a wide variety of applications. Current MQA models, whether model-based methods using top…
3D geometryGraph LearningReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment
We present ReHub, a novel graph transformer architecture that achieves linear complexity through an efficient reassignment technique between nodes and virtual nodes. Graph transformers have become increasingly important …
Graph LearningRevisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective
Self-supervised heterogeneous graph learning (SHGL) has shown promising potential in diverse scenarios. However, while existing SHGL methods share a similar essential with clustering approaches, they encounter two signif…
ClusteringGraph LearningSignal Processing over Time-Varying Graphs: A Systematic Review
As irregularly structured data representations, graphs have received a large amount of attention in recent years and have been widely applied to various real-world scenarios such as social, traffic, and energy settings. …
Graph LearningTime Series ForecastingAttribute-Enhanced Similarity Ranking for Sparse Link Prediction
Link prediction is a fundamental problem in graph data. In its most realistic setting, the problem consists of predicting missing or future links between random pairs of nodes from the set of disconnected pairs. Graph Ne…
AttributeBinary ClassificationGraph Learninggraph partitioning+2Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations
Graph Neural Networks (GNNs) have seen significant advances in recent years, yet their application to multigraphs, where parallel edges exist between the same pair of nodes, remains under-explored. Standard GNNs, designe…
Graph LearningFederated Continual Graph Learning
Managing evolving graph data presents substantial challenges in storage and privacy, and training graph neural networks (GNNs) on such data often leads to catastrophic forgetting, impairing performance on earlier tasks. …
Continual LearningGraph LearningTransfer LearningScale Invariance of Graph Neural Networks
We address two fundamental challenges in Graph Neural Networks (GNNs): (1) the lack of theoretical support for invariance learning, a critical property in image processing, and (2) the absence of a unified model capable …
Graph LearningNode ClassificationNode Classification on Non-Homophilic (Heterophilic) GraphsFedRGL: Robust Federated Graph Learning for Label Noise
Federated Graph Learning (FGL) is a distributed machine learning paradigm based on graph neural networks, enabling secure and collaborative modeling of local graph data among clients. However, label noise can degrade the…
Contrastive LearningGraph LearningTowards Data-centric Machine Learning on Directed Graphs: a Survey
In recent years, Graph Neural Networks (GNNs) have made significant advances in processing structured data. However, most of them primarily adopted a model-centric approach, which simplifies graphs by converting them int…
Graph LearningModel OptimizationSurveyContrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification
Graph contrastive learning has been successfully applied in text classification due to its remarkable ability for self-supervised node representation learning. However, explicit graph augmentations may lead to a loss of …
Contrastive LearningGraph AttentionGraph LearningRepresentation Learning+3Haar-Laplacian for directed graphs
This paper introduces a novel Laplacian matrix aiming to enable the construction of spectral convolutional networks and to extend the signal processing applications for directed graphs. Our proposal is inspired by a Haar…
DenoisingGraph LearningHeterophilic Graph Neural Networks Optimization with Causal Message-passing
In this work, we discover that causal inference provides a promising approach to capture heterophilic message-passing in Graph Neural Network (GNN). By leveraging cause-effect analysis, we can discern heterophilic edges …
Causal InferenceGraph LearningGraph Neural NetworkLink Prediction+1Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference
Heterogeneous Graph Neural Networks (HGNNs) have achieved promising results in various heterogeneous graph learning tasks, owing to their superiority in capturing the intricate relationships and diverse relational semant…
Graph LearningKnowledge DistillationAGLP: A Graph Learning Perspective for Semi-supervised Domain Adaptation
In semi-supervised domain adaptation (SSDA), the model aims to leverage partially labeled target domain data along with a large amount of labeled source domain data to enhance its generalization capability for the target…
Domain AdaptationGraph LearningSemi-supervised Domain AdaptationEfficient and Robust Continual Graph Learning for Graph Classification in Biology
Graph classification is essential for understanding complex biological systems, where molecular structures and interactions are naturally represented as graphs. Traditional graph neural networks (GNNs) perform well on st…
ClassificationGraph ClassificationGraph LearningTowards Federated Graph Learning in One-shot Communication
Federated Graph Learning (FGL) has emerged as a promising paradigm for breaking data silos among distributed private graphs. In practical scenarios involving heterogeneous distributed graph data, personalized Federated G…
Federated LearningGraph LearningNode ClassificationGraph Retention Networks for Dynamic Graphs
In this work, we propose Graph Retention Network as a unified architecture for deep learning on dynamic graphs. The GRN extends the core computational manner of retention to dynamic graph data as graph retention, which e…
GPUGraph LearningIMPaCT GNN: Imposing invariance with Message Passing in Chronological split Temporal Graphs
This paper addresses domain adaptation challenges in graph data resulting from chronological splits. In a transductive graph learning setting, where each node is associated with a timestamp, we focus on the task of Semi-…
Domain AdaptationGraph LearningNode ClassificationStochastic Block Model