Papers Graph structure learning
“Graph structure learning” 태그가 달린 논문 161편 · 필터 해제
Semi-Supervised Clustering via Dynamic Graph Structure Learning
Most existing semi-supervised graph-based clustering methods exploit the supervisory information by either refining the affinity matrix or directly constraining the low-dimensional representations of data points. The aff…
ClusteringGraph structure learningPosition-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topology-imbalance means and how to measure its…
Graph LearningGraph structure learningInductive BiasPositionGEDI: A Graph-based End-to-end Data Imputation Framework
Data imputation is an effective way to handle missing data, which is common in practical applications. In this study, we propose and test a novel data imputation process that achieve two important goals: (1) preserve the…
Graph structure learningImputationMeta-LearningPredictionDetecting Multivariate Time Series Anomalies with Zero Known Label
Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required. However, preparing such a dataset is very laborio…
Anomaly DetectionDensity EstimationGraph structure learningTime Series+3Boosting Graph Structure Learning with Dummy Nodes
With the development of graph kernels and graph representation learning, many superior methods have been proposed to handle scalability and oversmoothing issues on graph structure learning. However, most of those strateg…
Graph ClassificationGraph Representation LearningGraph structure learningRepresentation LearningGraph-DETR3D: Rethinking Overlapping Regions for Multi-View 3D Object Detection
3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding. Due to its low cost and high efficiency, multi-view 3D object detection has demonstrated promising appli…
3D Object DetectionGraph structure learningObjectobject-detection+2Multi-view graph structure learning using subspace merging on Grassmann manifold
Many successful learning algorithms have been recently developed to represent graph-structured data. For example, Graph Neural Networks (GNNs) have achieved considerable successes in various tasks such as node classifica…
Graph ClassificationGraph structure learningLink PredictionMULTI-VIEW LEARNING+1Exploring High-Order Structure for Robust Graph Structure Learning
Recent studies show that Graph Neural Networks (GNNs) are vulnerable to adversarial attack, i.e., an imperceptible structure perturbation can fool GNNs to make wrong predictions. Some researches explore specific properti…
Adversarial AttackGraph structure learningVocal Bursts Intensity PredictionSpatio-Temporal Latent Graph Structure Learning for Traffic Forecasting
Accurate traffic forecasting, the foundation of intelligent transportation systems (ITS), has never been more significant than nowadays due to the prosperity of smart cities and urban computing. Recently, Graph Neural Ne…
BenchmarkingGraph Neural NetworkGraph structure learningMold into a Graph: Efficient Bayesian Optimization over Mixed-Spaces
Real-world optimization problems are generally not just black-box problems, but also involve mixed types of inputs in which discrete and continuous variables coexist. Such mixed-space optimization possesses the primary c…
Bayesian OptimizationComputational EfficiencyGraph structure learningBalanced Graph Structure Learning for Multivariate Time Series Forecasting
Accurate forecasting of multivariate time series is an extensively studied subject in finance, transportation, and computer science. Fully mining the correlation and causation between the variables in a multivariate time…
Graph GenerationGraph LearningGraph structure learningMultivariate Time Series Forecasting+3Evidence-aware Fake News Detection with Graph Neural Networks
The prevalence and perniciousness of fake news has been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper, we focus on the evidence-based fake news…
Fake News DetectionGraph structure learningTowards Unsupervised Deep Graph Structure Learning
In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be deteriorated when noisy connections occur in the origina…
Contrastive LearningGraph structure learningNode ClassificationCompact Graph Structure Learning via Mutual Information Compression
Graph Structure Learning (GSL) recently has attracted considerable attentions in its capacity of optimizing graph structure as well as learning suitable parameters of Graph Neural Networks (GNNs) simultaneously. Current …
Graph structure learningGraph Structure Learning with Variational Information Bottleneck
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications. Most empirical studies of GNNs directly take the observed graph as input, assuming the observed structure perfectly depicts t…
Graph structure learningGraph Differentiable Architecture Search with Structure Learning
Discovering ideal Graph Neural Networks (GNNs) architectures for different tasks is labor intensive and time consuming. To save human efforts, Neural Architecture Search (NAS) recently has been used to automatically disc…
DenoisingGraph structure learningNeural Architecture SearchDeep Unsupervised Active Learning on Learnable Graphs
Recently deep learning has been successfully applied to unsupervised active learning. However, current method attempts to learn a nonlinear transformation via an auto-encoder while ignoring the sample relation, leaving h…
Active LearningGraph structure learningRelationRepresentation LearningPath Signature Area-Based Causal Discovery in Coupled Time Series
Coupled dynamical systems are frequently observed in nature, but often not well understood in terms of their causal structure without additional domain knowledge about the system. Especially when analyzing observational …
Causal DiscoveryGraph structure learningTime SeriesTime Series AnalysisAn Empirical Study: Extensive Deep Temporal Point Process
Temporal point process as the stochastic process on continuous domain of time is commonly used to model the asynchronous event sequence featuring with occurrence timestamps. Thanks to the strong expressivity of deep neur…
Graph structure learningVariational InferenceSpatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs
Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data. Recent works either rely on complicated discrete neural architectures or graph …
Graph structure learningRepresentation LearningTime SeriesTime Series Analysis