Summary of Training-free Heterogeneous Graph Condensation Via Data Selection, by Yuxuan Liang et al.
Training-free Heterogeneous Graph Condensation via Data Selection
by Yuxuan Liang, Wentao Zhang, Xinyi Gao, Ling Yang, Chong Chen, Hongzhi Yin, Yunhai Tong, Bin Cui
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper addresses the challenge of efficiently training large-scale heterogeneous graphs in real-world applications. Existing approaches simplify models to mitigate overhead, neglecting the importance of simplifying graphs from a data-centric perspective. The HGCond method introduces graph condensation for efficient model training but encounters limitations: low effectiveness due to reliance on simple relay models and low efficiency due to time-consuming condensing procedures. To address these challenges, the authors present FreeHGC, a Training-Free Heterogeneous Graph Condensation method that reformulates the condensation problem as a data selection issue. This approach leverages meta-paths to introduce a high-quality heterogeneous data selection criterion for selecting target-type nodes and designs two training-free condensation strategies for effective node synthesis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a huge network with many different types of connections. Right now, it’s hard to train models on these networks because they’re too big and complicated. The authors of this paper want to make it easier by creating a smaller version of the network that still has all the important information. They call this process “graph condensation.” But there are some problems with current methods: they don’t do a very good job, and it takes a long time. To fix these issues, the authors created a new way to condense graphs called FreeHGC. This method looks at the data in the graph and chooses the most important parts to keep. It’s like taking a big picture and deciding which details are most important to include. |
