Papers Graph structure learning
“Graph structure learning” 태그가 달린 논문 161편 · 필터 해제
Does Homophily Help in Robust Test-time Node Classification?
Homophily, the tendency of nodes from the same class to connect, is a fundamental property of real-world graphs, underpinning structural and semantic patterns in domains such as citation networks and social networks. Exi…
Graph structure learningNode ClassificationInterpretable Graph-Language Modeling for Detecting Youth Illicit Drug Use
Illicit drug use among teenagers and young adults (TYAs) remains a pressing public health concern, with rising prevalence and long-term impacts on health and well-being. To detect illicit drug use among TYAs, researchers…
Graph structure learningBeyond Softmax: A Natural Parameterization for Categorical Random Variables
Latent categorical variables are frequently found in deep learning architectures. They can model actions in discrete reinforcement-learning environments, represent categories in latent-variable models, or express relatio…
Graph structure learningReinforcement LearningSDGF: Fusing Static and Multi-Scale Dynamic Correlations for Multivariate Time Series Forecasting
Accurate multivariate time series forecasting hinges on inter-series correlations, which often evolve in complex ways across different temporal scales. Existing methods are limited in modeling these multi-scale dependenc…
Multivariate Time Series ForecastingGraph structure learningEnhancing Resilience for IoE: A Perspective of Networking-Level Safeguard
The Internet of Energy (IoE) integrates IoT-driven digital communication with power grids to enable efficient and sustainable energy systems. Still, its interconnectivity exposes critical infrastructure to sophisticated …
Graph structure learningAda-TransGNN: An Air Quality Prediction Model Based On Adaptive Graph Convolutional Networks
Accurate air quality prediction is becoming increasingly important in the environmental field. To address issues such as low prediction accuracy and slow real-time updates in existing models, which lead to lagging predic…
Graph structure learningGraph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning
Temporal graph learning is crucial for dynamic networks where nodes and edges evolve over time and new nodes continuously join the system. Inductive representation learning in such settings faces two major challenges: ef…
Graph structure learningRepresentation LearningGraph LearningDiscovering Spatial Correlations of Earth Observations for weather forecasting by using Graph Structure Learning
This study aims to improve the accuracy of weather predictions by discovering spatial correlations between Earth observations and atmospheric states. Existing numerical weather prediction (NWP) systems predict future atm…
Graph structure learningWeather ForecastingGraph Structure Learning with Privacy Guarantees for Open Graph Data
Publishing open graph data while preserving individual privacy remains challenging when data publishers and data users are distinct entities. Although differential privacy (DP) provides rigorous guarantees, most existing…
Graph structure learningA Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs
Temporal knowledge graph reasoning aims to predict future events with knowledge of existing facts and plays a key role in various downstream tasks. Previous methods focused on either graph structure learning or semantic …
Graph structure learningKnowledge GraphsPeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
Accurate weather forecasts are essential for supporting a wide range of activities and decision-making processes, as well as mitigating the impacts of adverse weather events. While traditional numerical weather predictio…
Deep LearningGraph structure learningImputationTime Series ForecastingMAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow
Knowledge components (KCs) are the fundamental units of knowledge in the field of education. A KC graph illustrates the relationships and dependencies between KCs. An accurate KC graph can assist educators in identifying…
Graph structure learningLanguage ModelingLanguage ModellingLarge Language ModelUnsupervised Graph Clustering with Deep Structural Entropy
Research on Graph Structure Learning (GSL) provides key insights for graph-based clustering, yet current methods like Graph Neural Networks (GNNs), Graph Attention Networks (GATs), and contrastive learning often rely hea…
ClusteringContrastive LearningGraph AttentionGraph Clustering+1GraphFLEx: Structure Learning Framework for Large Expanding Graphs
Graph structure learning is a core problem in graph-based machine learning, essential for uncovering latent relationships and ensuring model interpretability. However, most existing approaches are ill-suited for large-sc…
ClusteringGraph Neural NetworkGraph structure learningConcept Factorization via Self-Representation and Adaptive Graph Structure Learning
Concept Factorization (CF) models have attracted widespread attention due to their excellent performance in data clustering. In recent years, many variant models based on CF have achieved great success in clustering by t…
ClusteringGraph structure learningGRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype
Predicting genetic perturbations enables the identification of potentially crucial genes prior to wet-lab experiments, significantly improving overall experimental efficiency. Since genes are the foundation of cellular l…
Graph Neural NetworkGraph Representation LearningGraph structure learningLarge Language Model+1Rethinking Graph Structure Learning in the Era of LLMs
Recently, the emergence of LLMs has prompted researchers to integrate language descriptions into graphs, aiming to enhance model encoding capabilities from a data-centric perspective. This graph representation is called …
Graph LearningGraph structure learningIn-Context LearningTAGWeighted Graph Structure Learning with Attention Denoising for Node Classification
Node classification in graphs aims to predict the categories of unlabeled nodes by utilizing a small set of labeled nodes. However, weighted graphs often contain noisy edges and anomalous edge weights, which can distort …
ClassificationDenoisingGraph AttentionGraph structure learning+1Spatio-Temporal Graph Structure Learning for Earthquake Detection
Earthquake detection is essential for earthquake early warning (EEW) systems. Traditional methods struggle with low signal-to-noise ratios and single-station reliance, limiting their effectiveness. We propose a Spatio-Te…
Graph structure learningHeterogeneous Graph Structure Learning through the Lens of Data-generating Processes
Inferring the graph structure from observed data is a key task in graph machine learning to capture the intrinsic relationship between data entities. While significant advancements have been made in learning the structur…
Graph structure learning