Summary of Multi-view Subgraph Neural Networks: Self-supervised Learning with Scarce Labeled Data, by Zhenzhong Wang et al.
Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data
by Zhenzhong Wang, Qingyuan Zeng, Wanyu Lin, Min Jiang, Kay Chen Tan
First submitted to arxiv on: 19 Apr 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 This paper addresses a significant limitation in current graph neural networks (GNNs) by proposing a novel self-supervised learning framework, called multi-view subgraph neural networks (Muse). Traditional GNNs rely heavily on sufficient labeled samples, which is not always feasible in real-world applications. The authors demonstrate that leveraging subgraphs can capture long-range dependencies among nodes and alleviate the low-data regime. Muse identifies two types of subgraphs: local structure-focused and long-range dependency-focused. By fusing these views, Muse preserves topological properties of the graph, enhancing expressiveness for node classification tasks. Experimental results show that Muse outperforms alternative methods on limited-labeled data node classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps with a problem in machine learning called graph neural networks (GNNs). GNNs are good at classifying nodes in graphs, but they need lots of labeled data to work well. The authors propose a new way to train GNNs that doesn’t require as much labeled data. They do this by using smaller groups of connected nodes (called subgraphs) to capture long-range connections between nodes. This helps GNNs classify nodes even when there’s not enough labeled data. The new method, called Muse, works better than other methods in tests. |
Keywords
* Artificial intelligence * Classification * Machine learning * Self supervised
