Summary of Multi-scale Subgraph Contrastive Learning, by Yanbei Liu et al.
Multi-Scale Subgraph Contrastive Learning
by Yanbei Liu, Yu Zhao, Xiao Wang, Lei Geng, Zhitao Xiao
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 introduces a novel approach to graph-level contrastive learning, which learns representations for each graph by contrasting two augmented graphs. Unlike previous studies that assume an augmented graph is either positive or negative depending on its similarity with the original graph, this work reveals that the semantic information of an augmented graph structure may not be consistent with the original graph. The authors propose a multi-scale subgraph contrastive learning architecture that generates global and local views at different scales to provide richer self-supervised signals. Experimental results on eight real-world datasets demonstrate the effectiveness of the proposed method in characterizing fine-grained semantic information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn how graphs, like social networks or molecules, look similar or different from each other. Researchers have been using “augmented” graphs, which are like normal graphs but with some changes made to them. The question is, do these augmented graphs really capture the same information as the original graph? After studying this, the authors found that it’s not always true! They came up with a new approach called multi-scale subgraph contrastive learning that helps computers learn more about these complex structures. |
Keywords
» Artificial intelligence » Self supervised
