Graph structure learning
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Benchmarks
Cora
Most implemented
Graph Structure Learning for Robust Graph Neural Networks
PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation
DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning
Detecting Multivariate Time Series Anomalies with Zero Known Label
Papers
ReCoG: Reciprocal Co-Evolution for Multimodal Graph Learning
Multimodal graph learning requires jointly training over graph structure and heterogeneous node attributes, yet existing methods largely decouple these processes: prior multimodal graph neural networks (GNNs) focus on al…
Graph structure learningRepresentation LearningNode ClassificationLink PredictionSLeDGe: Semi-Supervised Learning on Data Streams with Graph Structure Learning
Semi-supervised learning (SSL) on data streams is challenging due to the continuous evolution of high-volume data and the scarcity of labels. Existing methods are limited in leveraging the intrinsic relationships among s…
Graph structure learningSpectral Sparsification of Laplacian-Constrained Gaussian and Hüsler-Reiss Graphical Models
Graph Laplacians encode graph structures in matrix form, and thus facilitate the application of linear algebra to graph theory. In statistics, two related families of probabilistic graphical models can be parameterized b…
Graph structure learningGraph LearningTopology-Aware Gaussian Graph Repair for Robust Graph Neural Networks
Graph neural networks have achieved strong performance on graph-structured data, but their effectiveness depends heavily on the quality of the observed graph. In real applications, graph topology is often imperfect: nois…
Graph structure learningGraph LearningIs Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
Relational prediction tasks are fundamental in many real-world applications, where data are naturally stored in relational databases (RDBs). Relational Deep Learning (RDL) addresses this problem by modeling RDBs as graph…
Graph structure learningInformative Graph Structure Learning
The quality of graph-structured data is fundamental to the success of modern graph analysis techniques such as Graph Neural Networks (GNNs). However, real-world graph data is often suboptimal, suffering from issues such …
Graph structure learning