Summary of Ion-c: Integration Of Overlapping Networks Via Constraints, by Praveen Nair et al.
ION-C: Integration of Overlapping Networks via Constraints
by Praveen Nair, Payal Bhandari, Mohammadsajad Abavisani, Sergey Plis, David Danks
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 proposes an efficient solution to a long-standing problem in causal learning. In many cases, variables are not measured simultaneously, but are distributed across multiple datasets with overlapping variables. The authors reformulate this problem as an answer set programming (ASP) problem and solve it using the clingo system. They evaluate their algorithm, called ION-C, on synthetic graphs and real-world data from the European Social Survey (ESS). The results show that overlap between subgraphs significantly impacts runtime, number of solution graphs, and agreement within the output set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in causal learning where variables are not all measured at once. Instead, they’re spread across different datasets with some variables repeated. The authors turn this problem into an easier-to-solve puzzle using computer programming language called answer set programming (ASP). They test their solution on fake graphs and real data from the European Social Survey to see how well it works. |
