Summary of Graph Partial Label Learning with Potential Cause Discovering, by Hang Gao et al.
Graph Partial Label Learning with Potential Cause Discovering
by Hang Gao, Jiaguo Yuan, Jiangmeng Li, Peng Qiao, Fengge Wu, Changwen Zheng, Huaping Liu
First submitted to arxiv on: 18 Mar 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 introduces Partial Label Learning (PLL) to address the challenges of graph representation learning for complex graph-structured data. PLL allows annotators to make errors, reducing the difficulty of data labeling. A novel graph representation learning method is proposed that utilizes potential cause extraction to obtain graph data holding causal relationships with labels. The approach conducts auxiliary training based on extracted graph data to eliminate interfering information in the PLL scenario. Theoretical analyses support the method’s rationale, and extensive evaluations and ablation studies demonstrate its superiority on multiple datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve a big problem in artificial intelligence called graph representation learning. Graphs are like pictures of relationships between things, but they’re really hard to work with because they’re so complex. To make it easier, the paper introduces a new way to label graphs, called Partial Label Learning (PLL). This means that people labeling the data don’t have to get everything right, which makes it much easier and faster. The paper also proposes a new method for learning from labeled graphs that is better than what’s currently available. It uses something called potential cause extraction to help eliminate confusing information in the data. The authors tested their method on several datasets and found that it outperforms existing methods. |
Keywords
* Artificial intelligence * Data labeling * Representation learning
