Summary of Gsina: Improving Subgraph Extraction For Graph Invariant Learning Via Graph Sinkhorn Attention, by Fangyu Ding et al.
GSINA: Improving Subgraph Extraction for Graph Invariant Learning via Graph Sinkhorn Attention
by Fangyu Ding, Haiyang Wang, Zhixuan Chu, Tianming Li, Zhaoping Hu, Junchi Yan
First submitted to arxiv on: 11 Feb 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 proposes a novel approach to graph invariant learning (GIL) that addresses the limitations of existing methods by leveraging Optimal Transport (OT) theory. The authors identify three key principles for extracting meaningful invariant subgraphs: sparsity, softness, and differentiability. To achieve these principles in one shot, they introduce Graph Sinkhorn Attention (GSINA), a graph attention mechanism that serves as a powerful regularization method for GIL tasks. GSINA enables the extraction of meaningful, differentiable invariant subgraphs with controllable sparsity and softness, making it a general framework for handling GIL tasks across multiple data grain levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding special patterns in graphs that stay the same even if the graph changes slightly. Right now, there are many ways to find these patterns, but they have some problems. The authors of this paper figured out what’s wrong with those methods and came up with a new way to find the patterns using a theory called Optimal Transport. They call their method Graph Sinkhorn Attention (GSINA) and it helps make sure the patterns are meaningful, easy to understand, and can be used in different situations. |
Keywords
* Artificial intelligence * Attention * One shot * Regularization
