Summary of Testing Dependency Of Weighted Random Graphs, by Mor Oren and Vered Paslev and Wasim Huleihel
Testing Dependency of Weighted Random Graphs
by Mor Oren, Vered Paslev, Wasim Huleihel
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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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 a novel approach to detecting edge dependencies between weighted random graphs. By formulating this task as a hypothesis testing problem, the authors establish thresholds for optimal testing based on the total number of nodes and weight distributions. The results reveal a statistical-computational gap that hinders efficient detection of edge dependencies. The study provides insights into information-theoretic limits of detecting edge dependencies between weighted random graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to detect connections between two types of networks (graphs) when the strength of these connections varies based on the nodes they connect. Think of it like trying to figure out if two people are more likely to be friends because they both know a mutual friend, or just by coincidence. The authors show that there’s a limit to how well we can do this, and that depends on the size of the networks and the strength of these connections. |
