Research map / Graph learning / Graph algorithms and clustering
Group and Graph Joint Sparsity for Linked Data Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use GGJ joint sparsity instead of g2 sparsity | sparse representation classifier · Gene | accuracy: 67.4597 -> 76.1694 | Helped |
| use GGJ joint sparsity instead of graph sparsity | sparse representation classifier · Gene | accuracy: 69.3669 -> 76.1694 | Helped |
| use GGJ joint sparsity instead of group sparsity | sparse representation classifier · Cora | accuracy: 90.8065 -> 91.9135 | Helped |
| use GGJ joint sparsity instead of g2 sparsity | sparse representation classifier · Twitter | accuracy: 85.5314 -> 85.8850 | Helped |
| use GGJ joint sparsity instead of g2 sparsity | sparse representation classifier · Protein | accuracy: 83.4779 -> 84.4239 | Helped |
| use GGJ joint sparsity instead of g2 sparsity | sparse representation classifier · Cora | F1: 70.4479 -> 72.0636 | Helped |
| use GGJ joint sparsity instead of g2 sparsity | sparse representation classifier · Twitter | F1: 71.0746 -> 72.3564 | Helped |
| use GGJ joint sparsity instead of graph sparsity | sparse representation classifier · Gene | F1: 75.0093 -> 78.7931 | Helped |
| use GGJ joint sparsity instead of g2 sparsity | sparse representation classifier · Protein | F1: 68.2048 -> 69.5269 | Helped |
| use g2 sparsity instead of group sparsity | sparse representation classifier · Gene | accuracy: 67.9839 -> 67.4597 | Hurt |
| use graph sparsity instead of group sparsity | sparse representation classifier · Protein | accuracy: 83.1668 -> 82.9579 | Hurt |
| use g2 sparsity instead of graph sparsity | sparse representation classifier · Gene | F1: 75.0093 -> 73.5754 | Hurt |
Closest papers by meaning
- Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks (ICLR 2023 · similarity 0.86)
- Graph Sparsification via Mixture of Graphs (ICLR 2025 · similarity 0.86)
- Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness (ICML 2024 · similarity 0.86)
- When Graph Structure Provably Helps Classification: Non-Asymptotic Recovery Guarantees (NeurIPS 2026 · similarity 0.85)
- EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification (ICML 2025 · similarity 0.84)
- Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks (NeurIPS 2026 · similarity 0.83)
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