Summary of Ftf-er: Feature-topology Fusion-based Experience Replay Method For Continual Graph Learning, by Jinhui Pang et al.
FTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph Learning
by Jinhui Pang, Changqing Lin, Xiaoshuai Hao, Rong Yin, Zixuan Wang, Zhihui Zhang, Jinglin He, Huang Tai Sheng
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 A novel method called Feature-Topology Fusion-based Experience Replay (FTF-ER) is proposed to mitigate the catastrophic forgetting issue with enhanced efficiency in continual graph learning. The approach combines feature and global topological information to maximize the utilization of comprehensive graph data, improving the effectiveness of sampled nodes. A novel module, Hodge Potential Score (HPS), calculates topological importance via Hodge decomposition on graphs, providing more accurate global topological information than neighbor sampling. FTF-ER achieves significant improvements in AA (3.6%) and AF (7.1%) on the OGB-Arxiv dataset compared to state-of-the-art methods, demonstrating its superior performance in class-incremental learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Continual graph learning is an important task that aims to update static GNNs to dynamic task flows. Experience replay (ER) is a popular method for this task. However, existing ER methods only consider local or global information and have limitations. A new approach called Feature-Topology Fusion-based Experience Replay (FTF-ER) is proposed to overcome these limitations. FTF-ER uses both feature and topological information to make better decisions. It also includes a new module that calculates the importance of nodes based on their position in the graph. This helps to reduce the amount of storage needed for the replay buffer and speeds up training. |
