Summary of Lamp: Learnable Meta-path Guided Adversarial Contrastive Learning For Heterogeneous Graphs, by Siqing Li et al.
LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs
by Siqing Li, Jin-Duk Park, Wei Huang, Xin Cao, Won-Yong Shin, Zhiqiang Xu
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper introduces a novel approach to heterogeneous graph neural networks (HGNNs) called LAMP, which aims to improve the performance of unsupervised learning on graph-structured data. The authors highlight that existing HGNN methods rely heavily on high-quality labels, which can be expensive to acquire, and that meta-path combinations significantly affect performance in unsupervised settings. To address these challenges, LAMP integrates various meta-path sub-graphs into a unified structure using an adversarial contrastive learning approach. This allows for edge pruning to maintain sparsity and enhance model performance and robustness. The authors demonstrate the effectiveness of LAMP through extensive experimental studies on four diverse datasets from the Heterogeneous Graph Benchmark (HGB), showing significant improvements in accuracy and robustness compared to existing state-of-the-art unsupervised models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn from graph data without labels. Right now, learning from graphs can be tricky because it relies on having good labels, which can be hard to get. The researchers found that the order in which they looked at different parts of the graph also mattered. They created a new method called LAMP that combines many different views of the graph into one. This helps them remove unnecessary information and improve their model’s performance. The authors tested LAMP on four different datasets and showed that it worked better than other methods. |
Keywords
» Artificial intelligence » Pruning » Unsupervised
