Summary of Adapting Unsigned Graph Neural Networks For Signed Graphs: a Few-shot Prompt Tuning Approach, by Zian Zhai et al.
Adapting Unsigned Graph Neural Networks for Signed Graphs: A Few-Shot Prompt Tuning Approach
by Zian Zhai, Sima Qing, Xiaoyang Wang, Wenjie Zhang
First submitted to arxiv on: 11 Dec 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 Signed Graph Neural Networks (SGNNs) excel in signed graph representation learning but suffer from limited generalization and reliance on labeled data. Recent advancements in “graph pre-training and prompt tuning” for Graph Neural Networks (GNNs) have reduced label dependence, improved generalization, and leveraged pre-training knowledge. However, these efforts focus solely on unsigned graphs, leaving a significant gap in signed graph datasets. To address this challenge, we propose Signed Graph Prompt Tuning (SGPT), which employs a graph template, semantic prompt, task template, and feature prompt to unify the pre-training and downstream phases, segregate mixed link semantics, adaptively integrate distinctive semantic information, reformulate downstream tasks, and ensure a consistent feature space. Our experiments on popular signed graph datasets demonstrate SGPT’s superiority over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have created a new way to use computers to understand complex networks called Signed Graph Neural Networks (SGNNs). These networks are great at learning about these networks, but they need labeled data and don’t work well in new situations. A few years ago, researchers found ways to make computer networks learn more without needing as much labeled data. However, this has only been done for ordinary networks, not the special kind of network that SGNNs deal with. To fix this, we came up with a new idea called Signed Graph Prompt Tuning (SGPT). It uses special templates and prompts to help computers understand these complex networks better. We tested SGPT on several big datasets and found it outperformed other methods. |
Keywords
» Artificial intelligence » Generalization » Prompt » Representation learning » Semantics
