Papers Graph structure learning
“Graph structure learning” 태그가 달린 논문 161편 · 필터 해제
Graph Structure Inference with BAM: Introducing the Bilinear Attention Mechanism
In statistics and machine learning, detecting dependencies in datasets is a central challenge. We propose a novel neural network model for supervised graph structure learning, i.e., the process of learning a mapping betw…
Graph structure learningDeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing
Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutio…
Contrastive LearningGraph structure learningNode ClassificationMolecular Property Prediction Based on Graph Structure Learning
Molecular property prediction (MPP) is a fundamental but challenging task in the computer-aided drug discovery process. More and more recent works employ different graph-based models for MPP, which have made considerable…
Drug DiscoveryGraph Neural NetworkGraph structure learningMolecular Property Prediction+2Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting
Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS). Recent studies offer evidence that the use of graph neural networks to capture laten…
Contrastive LearningCorrelated Time Series ForecastingGraph structure learningSelf-Learning+3Label-Free Multivariate Time Series Anomaly Detection
Anomaly detection in multivariate time series (MTS) has been widely studied in one-class classification (OCC) setting. The training samples in OCC are assumed to be normal, which is difficult to guarantee in practical si…
Anomaly DetectionDensity EstimationGraph structure learningOne-Class Classification+3Neural Gaussian Similarity Modeling for Differential Graph Structure Learning
Graph Structure Learning (GSL) has demonstrated considerable potential in the analysis of graph-unknown non-Euclidean data across a wide range of domains. However, constructing an end-to-end graph structure learning mode…
Graph structure learningSAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting
Spatio-temporal forecasting in various domains, like traffic prediction and weather forecasting, is a challenging endeavor, primarily due to the difficulties in modeling propagation dynamics and capturing high-dimensiona…
Graph LearningGraph structure learningSpatio-Temporal ForecastingTraffic Prediction+1Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on …
Graph structure learningPredictionProperty PredictionProtein Function Prediction+1GSLB: The Graph Structure Learning Benchmark
Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite t…
Graph structure learningRDGSL: Dynamic Graph Representation Learning with Structure Learning
Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise. Noise can …
Graph Representation LearningGraph structure learningRepresentation LearningUGSL: A Unified Framework for Benchmarking Graph Structure Learning
Graph neural networks (GNNs) demonstrate outstanding performance in a broad range of applications. While the majority of GNN applications assume that a graph structure is given, some recent methods substantially expanded…
BenchmarkingGraph structure learningGraph Structural Residuals: A Learning Approach to Diagnosis
Traditional model-based diagnosis relies on constructing explicit system models, a process that can be laborious and expertise-demanding. In this paper, we propose a novel framework that combines concepts of model-based …
DiagnosticGraph structure learningHomophily-enhanced Structure Learning for Graph Clustering
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results. Despite the success of existing GNN-based graph clustering methods, t…
ClusteringGraph ClusteringGraph structure learningTransferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities
Graph-based deep learning models are powerful in modeling spatiotemporal graphs for traffic forecasting. In practice, accurate forecasting models rely on sufficient traffic data, which may not be accessible in real-world…
Graph structure learningKnowledge DistillationTransfer LearningRobust Graph Structure Learning with the Alignment of Features and Adjacency Matrix
To improve the robustness of graph neural networks (GNN), graph structure learning (GSL) has attracted great interest due to the pervasiveness of noise in graph data. Many approaches have been proposed for GSL to jointly…
Graph structure learningGraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks
Graph structure learning is a well-established problem that aims at optimizing graph structures adaptive to specific graph datasets to help message passing neural networks (i.e., GNNs) to yield effective and robust node …
Graph structure learningOpenGSL: A Comprehensive Benchmark for Graph Structure Learning
Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptim…
Graph structure learningRepresentation LearningNodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
Graph neural networks have been extensively studied for learning with inter-connected data. Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing, heterophily, handling long-range depend…
Graph structure learningimage-classificationImage ClassificationNode ClassificationTime-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs
Temporal Graph Learning, which aims to model the time-evolving nature of graphs, has gained increasing attention and achieved remarkable performance recently. However, in reality, graph structures are often incomplete an…
Contrastive LearningData AugmentationGraph LearningGraph structure learning+1TabGSL: Graph Structure Learning for Tabular Data Prediction
This work presents a novel approach to tabular data prediction leveraging graph structure learning and graph neural networks. Despite the prevalence of tabular data in real-world applications, traditional deep learning m…
Contrastive LearningGraph Neural NetworkGraph structure learningPrediction