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 [1].
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. |