Summary of Graph Structure Learning with Bi-level Optimization, by Nan Yin
Graph Structure Learning with Bi-level Optimization
by Nan Yin
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to Graph Structure Learning (GSL) for Graph Neural Networks (GNNs), called Generic Structure Extraction with Bi-level Optimization for Graph Structure Learning (GSEBO). The authors argue that current GSL methods suffer from local structure heterogeneity, which can be addressed by jointly learning the graph structure and common GNN parameters from a global view. They introduce a generic structure extractor to abstract the graph structure and transform GNNs into learning both structure and common parameters. The learning process is modeled as a bi-level optimization problem, where the upper level optimizes GNN parameters to obtain global mapping information and the lower level optimizes graph structure using learned global information. Experimental results on four real-world datasets demonstrate the effectiveness of GSEBO in comparison to state-of-the-art GSL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way to improve Graph Neural Networks (GNNs) by learning how to understand the structure of complex graphs. Current methods only consider local information, but this can be limiting when the graph has different types of connections between nodes. The authors propose a new approach that looks at the graph from a global perspective and learns both the graph’s structure and the GNN’s common parameters. They tested their method on four real-world datasets and showed it performs better than existing methods. |
Keywords
* Artificial intelligence * Gnn * Optimization
