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..
Lightweight Label Propagation for Large-Scale Network Data
Authors: De-Ming Liang, Yu-Feng Li
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carry out three tasks to evaluate SLP. The first task is large-scale network analysis. In the second one, we compare SLP to several state-of-the-art algorithms on a categorization dataset. Finally, we show the influence of the density of graph on the performance of SLP. |
| Researcher Affiliation | Academia | De-Ming Liang and Yu-Feng Li National Key Laboratory for Novel Software Technology, Nanjing University Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023, China EMAIL, |
| Pseudocode | Yes | Algorithm 1 SLP Framework |
| Open Source Code | No | No explicit statement or link indicating that the authors' own source code for SLP is publicly available. |
| Open Datasets | Yes | We collect three large-scale network datasets and perform label propagation on them. These graphs [Yang and Leskovec, 2015; Yin et al., 2017] include graphs from Wikipedia1, Livejournal2 and Orkut3. |
| Dataset Splits | No | No explicit train/validation/test dataset splits were provided. It mentions "0.1%, 0.2%, 0.4%, 0.8%, 1.6%, 3.2% of instances are randomly selected as labeled data" for experiments, but this is about the proportion of labeled data, not a general data split for model development and evaluation. |
| Hardware Specification | Yes | We run these evaluations on a PC with 3.2GHz AMD Ryzen 1400 CPU and 16GB RAM. |
| Software Dependencies | No | No specific software dependencies with version numbers were provided. |
| Experiment Setup | Yes | We use default hyper-parameters for SLP. The step size η is set to 1/10^t where t is the next step to take and epoch T is set to 6. |