Summary of Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning, by Zhixiang Shen et al.
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning
by Zhixiang Shen, Shuo Wang, Zhao Kang
First submitted to arxiv on: 25 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 The Unsupervised Multiplex Graph Learning (UMGL) paper presents a novel framework, Information-aware Unsupervised Multiplex Graph Fusion (InfoMGF), to learn node representations on various edge types without manual labeling. The existing research overlooks the reliability of graph structure and relies heavily on contrastive learning, which limits its performance in realistic scenarios. InfoMGF uses graph structure refinement to eliminate task-irrelevant noise and maximize view-shared and view-unique task-relevant information, tackling non-redundant multiplex graphs. Theoretical analyses guarantee effectiveness, and comprehensive experiments demonstrate superior performance and robustness compared to various baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Unsupervised Multiplex Graph Learning is a new way to learn about relationships between things in complex networks without needing human labels. Right now, this kind of learning doesn’t take into account that real-world data can be noisy and contain information that’s not helpful for the task at hand. This paper proposes a new approach called InfoMGF that improves upon existing methods by removing irrelevant noise and keeping important information. The authors tested their method on various tasks and found it outperformed other approaches, even ones that used labeled training data. |
Keywords
» Artificial intelligence » Unsupervised
