Summary of A Sampling-based Framework For Hypothesis Testing on Large Attributed Graphs, by Yun Wang et al.
A Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs
by Yun Wang, Chrysanthi Kosyfaki, Sihem Amer-Yahia, Reynold Cheng
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Databases (cs.DB); 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 proposed framework formalizes node, edge, and path hypotheses in attributed graphs, developing a sampling-based hypothesis testing method that can incorporate existing graph sampling techniques. The Path-Hypothesis-Aware SamplEr (PHASE) is an m-dimensional random walk that takes into account paths specified in a hypothesis, improving accuracy and efficiency. The framework includes PHASEopt for optimized time efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us test hypotheses about populations using data from graphs, which are common representations of real-life applications. The authors formalize different types of hypotheses in graph data and create a new way to select samples that takes into account the paths mentioned in these hypotheses. This approach is more accurate and efficient than previous methods. |
