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..
Unveiling Environmental Sensitivity of Individual Gains in Influence Maximization
Authors: Xinyan Su, Zhiheng Zhang, Jiyan Qiu, Zhaojuan Yue, Jun Li
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, experiments on synthetic and real-world datasets validate the effectiveness and reliability of our approach. |
| Researcher Affiliation | Academia | 1Computer Network Information Center, Chinese Academy of Sciences, Beijing, China 2University of Chinese Academy of Sciences, Beijing, China 3School of Statistics and Data Science, Shanghai University of Finance and Economics, Shanghai 200433, P.R. China 4Institute of Data Science and Statistics, Shanghai University of Finance and Economics, Shanghai 200433, P.R. China |
| Pseudocode | Yes | Algorithm 1: G-Cau IM |
| Open Source Code | Yes | Further methodological details, extended analyses, additional experiments, and core codes are provided in https://github.com/suxinyan/cauim3236/. |
| Open Datasets | Yes | Our real-world data comprises three real-world public datasets: Good Reads 8, Contact [39], and Email-Eu [4]. |
| Dataset Splits | No | The paper mentions using "synthetic and real-world datasets" but does not specify exact training/test/validation splits, percentages, or sample counts for these datasets. |
| Hardware Specification | No | Our experiments are conducted on Linux operating system with Python 3.10.14, torch 2.1. |
| Software Dependencies | Yes | Our experiments are conducted on Linux operating system with Python 3.10.14, torch 2.1. |
| Experiment Setup | Yes | For basic hyperparameters, we set seed number K = 15 and spread probability PSICP as 0.01 ( denoted as p for simplicity). |