Summary of Score-matching-based Structure Learning For Temporal Data on Networks, by Hao Chen et al.
Score-matching-based Structure Learning for Temporal Data on Networks
by Hao Chen, Kai Yi, Lin Liu, Yu Guang Wang
First submitted to arxiv on: 10 Dec 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 This paper introduces a new algorithm for causal discovery, called Parent Identification-based Causal structure learning for both i.i.d. and temporal data on networKs (PICK). The score-matching method has shown excellent performance in identifying causality from empirical data, but existing algorithms struggle with high computational complexity due to the pruning step required when handling dense Directed Acyclic Graphs (DAGs). To address this issue, PICK develops a new parent-finding subroutine for leaf nodes in DAGs, significantly accelerating the most time-consuming part of the process. This improvement enables PICK to efficiently analyze static and temporal data on networks with weak network interference, while maintaining high accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to find causes from big datasets! Right now, we have good ways to do this, but they’re not super fast for really big datasets. The new algorithm, called PICK, helps speed up the process by finding important parts of the dataset more quickly. This is helpful because lots of data in science and industry has connections between things (like how people interact on social media). PICK can handle these types of datasets and find causes even when they’re changing over time. |
Keywords
* Artificial intelligence * Pruning
