Summary of Scalable Deep Metric Learning on Attributed Graphs, by Xiang Li et al.
Scalable Deep Metric Learning on Attributed Graphs
by Xiang Li, Gagan Agrawal, Ruoming Jin, Rajiv Ramnath
First submitted to arxiv on: 20 Nov 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 This paper proposes a novel graph embedding method that can handle large attributed graphs and supports multiple downstream learning tasks. The approach extends deep metric and unbiased contrastive learning techniques to accommodate attributes and enable mini-batch processing while achieving scalability. Two algorithms are presented: DMT for semi-supervised learning and DMAT-i for unsupervised cases. A generalization bound is provided for node classification and tuplet loss is linked to contrastive learning for the first time. Extensive experiments demonstrate high scalability of representation construction, outperforming existing methods in three downstream tasks (node clustering, node classification, and link prediction). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how to create special maps for big graphs that have lots of information attached to each point. These maps can help with many different tasks, like classifying the points or predicting connections between them. The method uses a combination of deep learning techniques to make sure the map is accurate and efficient. Two ways to use this method are presented: one for when we have some labeled data and another for when we don’t. This helps us understand how well our maps will work in different situations. Overall, the method does a great job of creating consistent and useful maps that can help us with many tasks. |
Keywords
» Artificial intelligence » Classification » Clustering » Deep learning » Embedding » Generalization » Semi supervised » Unsupervised
