Graph structure learning
52 papers with code • 1 benchmarks • 2 datasets
Semi-supervised node classification when a graph structure is not available.
Libraries
Use these libraries to find Graph structure learning models and implementations
Cora
ChEMBL
Most implemented papers
Graph Structure Learning for Robust Graph Neural Networks
A natural idea to defend adversarial attacks is to clean the perturbed graph.
Graph-Bert: Only Attention is Needed for Learning Graph Representations
We have tested the effectiveness of GRAPH-BERT on several graph benchmark datasets.
Compact Graph Structure Learning via Mutual Information Compression
Furthermore, we maintain the performance of estimated views and the final view and reduce the mutual information of every two views.
Detecting Multivariate Time Series Anomalies with Zero Known Label
zqhang/detecting-multivariate-time-series-anomalies-with-zero-known-label
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Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required.
DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning
Kaleidophon/deep-significance
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Graph neural networks (GNNs) have demonstrated success in learning representations of brain graphs derived from functional magnetic resonance imaging (fMRI) data.
A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation
enoche/freedom
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Based on this finding, we propose a simple yet effective model, dubbed as FREEDOM, that FREEzes the item-item graph and DenOises the user-item interaction graph simultaneously for Multimodal recommendation.
SE-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.
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension
The proposed GraphFlow model can effectively capture conversational flow in a dialog, and shows competitive performance compared to existing state-of-the-art methods on CoQA, QuAC and DoQA benchmarks.
Variationally Regularized Graph-based Representation Learning for Electronic Health Records
A feasible approach to improving the representation learning of EHR data is to associate relevant medical concepts and utilize these connections.
Deep Iterative and Adaptive Learning for Graph Neural Networks
hugochan/IDGL
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In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously.
