Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Federated Graph Learning with Adaptive Importance-based Sampling
For privacy-preserving graph learning tasks involving distributed graph datasets, federated learning (FL)-based GCN (FedGCN) training is required. A key challenge for FedGCN is scaling to large-scale graphs, which typica…
Federated LearningGraph LearningGraph SamplingPrivacy PreservingHydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learning
Hydrometric forecasting is crucial for managing water resources, flood prediction, and environmental protection. Water stations are interconnected, and this connectivity influences the measurements at other stations. How…
Graph Learningimage-classificationImage ClassificationWhen Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning
Capitalizing on the intuitive premise that shape characteristics are more robust to perturbations, we bridge adversarial graph learning with the emerging tools from computational topology, namely, persistent homology rep…
Graph LearningOnline Proximal ADMM for Graph Learning from Streaming Smooth Signals
Graph signal processing deals with algorithms and signal representations that leverage graph structures for multivariate data analysis. Often said graph topology is not readily available and may be time-varying, hence (d…
Graph LearningGraph structure learningFedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction
Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. Despite its broad applications in fields …
Dimensionality ReductionFederated LearningGraph LearningFlexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning
The ability of Graph Neural Networks (GNNs) to capture long-range and global topology information is limited by the scope of conventional graph Laplacian, leading to unsatisfactory performance on some datasets, particula…
Graph AttentionGraph LearningOnline Network Inference from Graph-Stationary Signals with Hidden Nodes
Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneously but also that all nodes can be obse…
Graph LearningOnline Learning Of Expanding Graphs
This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are crucial in delay-sensitive applications…
Graph LearningCliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs
Graph neural networks have become the default choice by practitioners for graph learning tasks such as graph classification and node classification. Nevertheless, popular graph neural network models still struggle to cap…
Graph ClassificationGraph LearningGraph Neural NetworkNode Classification+1Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph has no cycles of odd number of negative edges. Laplaci…
Graph LearningVirtual Node Generation for Node Classification in Sparsely-Labeled Graphs
In the broader machine learning literature, data-generation methods demonstrate promising results by generating additional informative training examples via augmenting sparse labels. Such methods are less studied in grap…
Graph LearningMeta-LearningNode ClassificationSelf-Supervised LearningMCDGLN: Masked Connection-based Dynamic Graph Learning Network for Autism Spectrum Disorder
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by complex physiological processes. Previous research has predominantly focused on static cerebral interactions, often neglecting the brain's …
Functional ConnectivityGraph LearningLATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning
Graph Contrastive Learning (GCL) is a potent paradigm for self-supervised graph learning that has attracted attention across various application scenarios. However, GCL for learning on Text-Attributed Graphs (TAGs) has y…
Contrastive LearningData AugmentationGraph LearningLanguage Modelling+1Towards Faster Graph Partitioning via Pre-training and Inductive Inference
Graph partitioning (GP) is a classic problem that divides the node set of a graph into densely-connected blocks. Following the IEEE HPEC Graph Challenge and recent advances in pre-training techniques (e.g., large-languag…
Graph Learninggraph partitioningOpenFGL: A Comprehensive Benchmark for Federated Graph Learning
Federated graph learning (FGL) is a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing. This approach inherently involves large-scale distributed g…
Graph LearningDual Adversarial Perturbators Generate rich Views for Recommendation
Graph contrastive learning (GCL) has been extensively studied and leveraged as a potent tool in recommender systems. Most existing GCL-based recommenders generate contrastive views by altering the graph structure or intr…
Contrastive LearningGraph LearningRecommendation SystemsDisentangled Generative Graph Representation Learning
Recently, generative graph models have shown promising results in learning graph representations through self-supervised methods. However, most existing generative graph representation learning (GRL) approaches rely on r…
DisentanglementGraph LearningGraph Representation LearningRepresentation Learning+1LLM-enhanced Scene Graph Learning for Household Rearrangement
The household rearrangement task involves spotting misplaced objects in a scene and accommodate them with proper places. It depends both on common-sense knowledge on the objective side and human user preference on the su…
Common Sense ReasoningGraph LearningTask PlanningOptimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-Densely Connected GNNs
Federated Graph Learning (FGL) is an emerging technology that enables clients to collaboratively train powerful Graph Neural Networks (GNNs) in a distributed manner without exposing their private data. Nevertheless, FGL …
Graph LearningSlicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks
As graphs grow larger, full-batch GNN training becomes hard for single GPU memory. Therefore, to enhance the scalability of GNN training, some studies have proposed sampling-based mini-batch training and distributed grap…
GPUGraph LearningNode Classification