Papers Graph structure learning
“Graph structure learning” 태그가 달린 논문 161편 · 필터 해제
Collective Relational Inference for learning heterogeneous interactions
Interacting systems are ubiquitous in nature and engineering, ranging from particle dynamics in physics to functionally connected brain regions. These interacting systems can be modeled by graphs where edges correspond t…
Graph structure learningSE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it is susceptible to low-quality and unreliable structure, which has been a norm rather than an exception in real-world graphs. Ex…
Graph structure learningRepresentation LearningSemantic-Fused Multi-Granularity Cross-City Traffic Prediction
Accurate traffic prediction is essential for effective urban management and the improvement of transportation efficiency. Recently, data-driven traffic prediction methods have been widely adopted, with better performance…
Graph structure learningManagementNode ClusteringPrediction+3Structure Embedded Nucleus Classification for Histopathology Images
Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural netwo…
ClassificationGraph Neural NetworkGraph structure learningNuclei ClassificationSearch to Capture Long-range Dependency with Stacking GNNs for Graph Classification
In recent years, Graph Neural Networks (GNNs) have been popular in the graph classification task. Currently, shallow GNNs are more common due to the well-known over-smoothing problem facing deeper GNNs. However, they are…
Graph ClassificationGraph structure learningNeural Architecture SearchDrug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning
Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource in…
Graph structure learningSelf-organization Preserved Graph Structure Learning with Principle of Relevant Information
Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption cannot always be satisfied, as real-world …
Graph structure learningMegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling
Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the underlying heterogeneity and non-stationarity implied in the grap…
DecoderGraph LearningGraph structure learningMultivariate Time Series Forecasting+4Directed Acyclic Graph Structure Learning from Dynamic Graphs
Estimating the structure of directed acyclic graphs (DAGs) of features (variables) plays a vital role in revealing the latent data generation process and providing causal insights in various applications. Although there …
Graph structure learningSpatio-Temporal Meta-Graph Learning for Traffic Forecasting
Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community. To address the spatio-temporal heterogeneity and non-stationarity implied in the traf…
DecoderGraph LearningGraph structure learningMultivariate Time Series Forecasting+4Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in multivariate biosignals is challenging due …
ClassificationGraph Neural NetworkGraph structure learningSeizure Detection+2A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation
Multimodal recommender systems utilizing multimodal features (e.g., images and textual descriptions) typically show better recommendation accuracy than general recommendation models based solely on user-item interactions…
DenoisingGraph structure learningMulti-modal RecommendationMultimodal Recommendation+1Self-Supervised Graph Structure Refinement for Graph Neural Networks
Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning fra…
Contrastive LearningGPUGraph structure learningLink PredictionpyGSL: A Graph Structure Learning Toolkit
We introduce pyGSL, a Python library that provides efficient implementations of state-of-the-art graph structure learning models along with diverse datasets to evaluate them on. The implementations are written in GPU-fri…
GPUGraph LearningGraph structure learningRolling Shutter CorrectionHFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection
Network or physical attacks on industrial equipment or computer systems may cause massive losses. Therefore, a quick and accurate anomaly detection (AD) based on monitoring data, especially the multivariate time-series (…
Anomaly DetectionGraph structure learningRepresentation LearningSemi-supervised Anomaly Detection+4HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding
Embedded topic models are able to learn interpretable topics even with large and heavy-tailed vocabularies. However, they generally hold the Euclidean embedding space assumption, leading to a basic limitation in capturin…
Graph structure learningTopic ModelsRegularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting
Multivariate time-series forecasting is a critical task for many applications, and graph time-series network is widely studied due to its capability to capture the spatial-temporal correlation simultaneously. However, mo…
Graph GenerationGraph structure learningMultivariate Time Series ForecastingTime Series+2Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks
The prevalence and perniciousness of fake news have been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper, we focus on evidence-based fake news de…
Contrastive LearningFake News DetectionGraph structure learningRepresentation LearningRobust Graph Structure Learning via Multiple Statistical Tests
Graph structure learning aims to learn connectivity in a graph from data. It is particularly important for many computer vision related tasks since no explicit graph structure is available for images for most cases. A na…
Face ClusteringGraph structure learningDynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning
Graph neural networks (GNNs) have demonstrated success in learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data. However, existing GNN methods assume brain graphs are sta…
Functional ConnectivityGraph structure learning