Summary of Eugene: Explainable Unsupervised Approximation Of Graph Edit Distance with Generalized Edit Costs, by Aditya Bommakanti et al.
EUGENE: Explainable Unsupervised Approximation of Graph Edit Distance with Generalized Edit Costs
by Aditya Bommakanti, Harshith Reddy Vonteri, Sayan Ranu, Panagiotis Karras
First submitted to arxiv on: 8 Feb 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 The proposed algorithm, EUGENE, addresses the challenge of accurately estimating Graph Edit Distance (GED) between query graphs and those with similar structures. Traditional methods often rely on neural networks, which have limitations such as requiring ground-truth GEDs for training and being dataset-specific. EUGENE, an algebraic approach, not only estimates GED but also provides edit paths corresponding to the approximated cost. The method demonstrates state-of-the-art performance in GED estimation and scalability across various datasets and cost settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EUGENE is a new way to measure how similar two graphs are to each other. Graphs are like networks, and this algorithm helps us find similarities between them. Right now, we use special computer programs called neural networks to do this, but they have some problems. EUGENE solves these issues by being more efficient and providing extra information about why the graphs are similar. |
