Summary of Optimizing the Induced Correlation in Omnibus Joint Graph Embeddings, by Konstantinos Pantazis et al.
Optimizing the Induced Correlation in Omnibus Joint Graph Embeddings
by Konstantinos Pantazis, Michael Trosset, William N. Frost, Carey E. Priebe, Vince Lyzinski
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
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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 paper investigates joint graph embedding algorithms, which create correlations across networks in the embedding space. The Omnibus framework previously demonstrated the dual effects of algorithm-induced and model-inherent correlations on embedded network correlations. To address these issues, this work presents automated Omnibus construction methods to solve two key problems: correlation-to-OMNI and flat correlation. In the flat correlation problem, a lower bound is proved for the minimum algorithm-induced flat correlation, and the classical Omnibus construction is shown to induce the maximal flat correlation. For the correlation-to-OMNI problem, an algorithm named corr2Omni estimates generalized Omnibus weights that induce optimal correlations in the embedding space. The proposed methods are tested on simulated and real-world data, demonstrating increased effectiveness over traditional Omnibus constructions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to connect different networks together. Right now, we can create connections between these networks, but it’s not perfect. The algorithm that does this creates extra connections that aren’t really there. To fix this, the authors developed two new methods that make better connections. One method helps us understand what the minimum number of extra connections is, and the other method helps us create the best possible connections given the data we have. The authors tested these new methods on made-up data and real-world data, and they found that they work better than the old way. |
Keywords
» Artificial intelligence » Embedding » Embedding space
