Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Directed Probabilistic Watershed
Authors: Enrique Fita Sanmartin, Sebastian Damrich, Fred A. Hamprecht
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we run an illustrative experiment to show the performance of the DProb WS on node classification. ... We compare the DProb WS with the methods exposed in [9, 11, 49] referred as ARW, GTG and LLUD respectively. |
| Researcher Affiliation | Academia | Enrique Fita Sanmartín, Sebastian Damrich, Fred A. Hamprecht HCI/IWR at Heidelberg University, 69120 Heidelberg, Germany {enrique.fita.sanmartin, sebastian.damrich, fred.hamprecht} @iwr.uni-heidelberg.de |
| Pseudocode | Yes | Algorithm 1: DProb WS |
| Open Source Code | Yes | Code publicly available at https://github.com/hci-unihd/Directed_Probabilistic_ Watershed.git |
| Open Datasets | Yes | We construct k NN graphs, with k = 5, from the UCI datasets [10] Digits [44] and 20Newsgroups[23]. Additionally we consider the Email-EUnetwork [26, 27, 46], the Cora network[29] and Citeseer X network[33]. |
| Dataset Splits | No | The paper describes how labeled nodes are sampled as seeds ('sampling a certain fraction r of all nodes from each class uniformly as seeds') but does not specify distinct training, validation, and test splits or a cross-validation setup. |
| Hardware Specification | No | The paper does not specify the hardware used for the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We construct k NN graphs, with k = 5... Inspired by [9], we sample a certain fraction r of all nodes from each class uniformly as seeds. In Figure 3, we show the average accuracy over 20 runs for each of the r values between 0.1 and 0.9. |