Summary of Graph Data Condensation Via Self-expressive Graph Structure Reconstruction, by Zhanyu Liu et al.
Graph Data Condensation via Self-expressive Graph Structure Reconstruction
by Zhanyu Liu, Chaolv Zeng, Guanjie Zheng
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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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 The proposed framework, GCSR, addresses the limitations of existing graph data condensation methods by explicitly incorporating the original graph structure into the condensing process and reconstructing an interpretable self-expressive graph structure. This method condenses large-scale graphs to smaller synthetic graphs while preserving essential information for training downstream GNNs. The authors introduce a novel framework that leverages the information of the original graph structure, capturing nuanced interdependencies between condensed nodes. The efficacy of GCSR is validated through extensive experiments and comprehensive analysis across diverse GNN models and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GCSR is a new way to shrink big graphs into smaller ones while keeping important details for training special kinds of artificial intelligence called Graph Neural Networks (GNNs). Existing methods only focused on one aspect, like node features or the graph structure. This new method combines both and makes sure the original graph’s information is used. It also creates an easy-to-understand graph structure for the shrunk dataset. The authors tested this method with different GNN models and datasets and it worked well. |
Keywords
* Artificial intelligence * Gnn
