Summary of Task-oriented Gnns Training on Large Knowledge Graphs For Accurate and Efficient Modeling, by Hussein Abdallah et al.
Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling
by Hussein Abdallah, Waleed Afandi, Panos Kalnis, Essam Mansour
First submitted to arxiv on: 9 Mar 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 The proposed approach, KG-TOSA, automates the extraction of task-oriented subgraphs (TOSGs) for heterogeneous graph neural networks (HGNNs) on large knowledge graphs. By defining a generic graph pattern that captures local and global structure relevant to a specific task, KG-TOSA reduces preprocessing overhead and helps state-of-the-art HGNN methods achieve up to 70% time and memory usage reductions while improving model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary To solve problems on large knowledge graphs (KGs), researchers often handcraft subgraphs that are relevant to the task. This approach can be challenging and time-consuming, as it requires a deep understanding of the KG’s structure and the task’s objectives. The proposed solution, KG-TOSA, automates this process by defining a generic graph pattern that captures local and global structure relevant to a specific task. |
