Sampling for Beyond-Worst-Case Online Ranking
Authors: Qingyun Chen, Sungjin Im, Benjamin Moseley, Chenyang Xu, Ruilong Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results validate the theory and show that such algorithms can be used on temporal data to obtain strong results. This section validates the empirical performance of our sampling models on real datasets. We design two experiments. |
| Researcher Affiliation | Academia | 1 Electrical Engineering and Computer Science, University of California at Merced 2 Tepper School of Business, Carnegie Mellon University 3 Shanghai Key Laboratory of Trustworthy Computing, East China Normal University 4 Department of Computer Science and Engineering, University at Buffalo |
| Pseudocode | Yes | Algorithm 1: Main Algorithm for Online Minimum Feedback Arc Set, Algorithm 2: construct-tree(T, r, v), Algorithm 3: Over-&Under-sampling Algorithm |
| Open Source Code | No | The paper does not include an explicit statement about releasing the source code for its methodology or provide a link to a code repository. |
| Open Datasets | Yes | The experiments consider three directed temporal network datasets with different sizes: College Msg1 (|V | = 1, 899, |E| = 59, 835), Math Overflow2 (|V | = 24, 818, |E| = 506, 550), and Reddit Hyperlink3 (|V | = 55, 863, |E| = 858, 490). 1https://snap.stanford.edu/data/College Msg.html 2https://snap.stanford.edu/data/sx-mathoverflow.html 3https://snap.stanford.edu/data/soc-Reddit Hyperlinks.html |
| Dataset Splits | No | The paper describes using specific datasets and varying sampling fractions (λ) and arrival orders, but it does not specify traditional training, validation, or test dataset splits (e.g., as percentages or counts) for model training or evaluation, as the problem is structured differently from typical supervised learning tasks. |
| Hardware Specification | Yes | The experiments are conducted on a machine running Ubuntu 18.04 with an i7-7800X CPU and 48 GB memory. |
| Software Dependencies | No | The paper mentions the operating system ("Ubuntu 18.04") but does not provide specific version numbers for any programming languages, libraries, frameworks, or solvers used in the experiments. |
| Experiment Setup | Yes | The experimental results are averaged over 10 runs. ... We consider λ {0, 0.001, 0.003, 0.027, 0.081, 0.243, 0.729, 1} ... The experiments investigate three arrival orders of vertices in the graphs. First of all, we consider the chronological order given by the data to simulate the performance of our sampling models in practice. Then we use the bad triangle decreasing order and the indegree decreasing order to approximate the adversarial order. |