Summary of Promptgcn: Bridging Subgraph Gaps in Lightweight Gcns, by Shengwei Ji et al.
PromptGCN: Bridging Subgraph Gaps in Lightweight GCNs
by Shengwei Ji, Yujie Tian, Fei Liu, Xinlu Li, Le Wu
First submitted to arxiv on: 14 Oct 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 paper proposes a novel prompt-based lightweight Graph Convolutional Network (GCN) model called PromptGCN, designed to bridge the gaps among subgraphs in large-scale graph-based applications. The existing subgraph sampling methods reduce memory consumption but suffer from reduced accuracy due to lack of global graph information. PromptGCN uses learnable prompt embeddings to obtain global information and attaches prompts to each subgraph to transfer this information. Experimental results on seven large-scale graphs demonstrate superior performance compared to baselines, with an improvement in accuracy of up to 5.48% on the Flickr dataset. The proposed model can be easily combined with any subgraph sampling method to achieve a lightweight GCN model with higher accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PromptGCN is a new approach that helps fix problems with big graphs and deep neural networks. Currently, these models use too much memory and sometimes run out of space on regular computers. To solve this, some people suggested breaking the graph into smaller pieces and training the network on each piece separately. However, this method has limitations because it can’t take into account the entire graph at once. The new PromptGCN model tries to fix this by using special “prompt” information that can be shared between the different parts of the graph. This allows the neural network to learn more accurately and efficiently. |
Keywords
» Artificial intelligence » Convolutional network » Gcn » Neural network » Prompt
