Efficient Nonparametric Subgraph Detection Using Tree Shaped Priors
Authors: Nannan Wu, Feng Chen, Jianxin Li, Baojian Zhou, Naren Ramakrishnan
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, using extensive experiments we demonstrate the performance of our proposed algorithms in two real-world application domains (water pollution detection in water sensor networks and spatial event detection in social media networks) and contrast against state-of-the-art connected subgraph detection methods. |
| Researcher Affiliation | Academia | Dept. of Computer Science & Engineering, Beihang University, Beijing 100191, China Dept. of Informatics, University at Albany, SUNY, Albany, NY 12203 Dept. of Computer Science, Virginia Tech, Arlington, VA 22203 |
| Pseudocode | Yes | Algorithm 1: Tree-Shaped-Priors Subgraph Detection |
| Open Source Code | Yes | Wu, N.; Chen, F.; Li, J.; Zhou, B.; and Ramakrishnan, N. Appendix, 2005: http://www.cs.albany.edu/ fchen/aaaiap.pdf. |
| Open Datasets | Yes | 1) Water Pollution Dataset. The Battle of the Water Sensor Networks (BWSN) (Ostfeld and Salomons 2008) provides a real-world network... 2) Event Detection Dataset. ...Gold Standard Reports (GSR) of 4279 official haze outbreak records (level 3) were collected from official websites (MEP ), and each GSR record was formatted as ( Time(YYYYMMDD) , Location(Province) ) |
| Dataset Splits | No | The paper describes the datasets used and evaluation metrics, but it does not explicitly provide details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions tools like EPANET and Weibo data, but it does not provide specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | Yes | Specifically, for Event Tree and Graph-Laplacian, we tested the set of λ values: {0.1, 0.2, , 1.0, 50, 100, , 1500}. As Event Tree requires edge weights, we define the weight of an edge in the water pipeline network as the length of the pipeline segment to the edge and define the weight of an edge in the user-user network of Weibo as 1 without a better way. Two nonparametric scan statistics, HC and BJ, were evaluated. We set the parameters αmax 0.15 and the number of seed nodes C 5 for NPHGS and our methods. |