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..
Semi-Data-Driven Network Coarsening
Authors: Li Gao, Jia Wu, Hong Yang, Zhi Qiao, Chuan Zhou, Yue Hu
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic and real-world data sets demonstrate the quality and effectiveness of the proposed method. |
| Researcher Affiliation | Collaboration | Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China Quantum Computation & Intelligent Systems Centre, University of Technology Sydney, Australia ]MathWorks, Beijing, China Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China |
| Pseudocode | Yes | Algorithm 1: Network Node Label Distribution Learning Algorithm 2: The Semi-Net Coarsen(G, C, ) algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for their methodology is open-source or publicly available. |
| Open Datasets | Yes | For synthetic data, we consider the Kronecker graph model [Leskovec et al., 2010]... For real-world data, we collect Twitter data1 [Zhang et al., 2013]. 1http://aminer.org/billboard/Influencelocality |
| Dataset Splits | No | The paper tunes parameters on a 'sub-graph with 1,000 nodes and 2,000 cascade data for each dataset' but does not specify explicit train/validation/test dataset splits with percentages or counts for the main datasets used in the experiments. |
| Hardware Specification | Yes | All experiments are conducted on a Linux system with 6 cores 1.4GHZ AMD CPUs and 32GB memory. |
| Software Dependencies | No | The paper mentions adapting 'Accelerated Proximal Gradient (APG)' but does not list specific software libraries or their version numbers used in the implementation. |
| Experiment Setup | Yes | The parameter λ is searched from λ 2 {0.1, 1, 10, 100}, λy is searched from λy 2 {0.01, 0.1, 1}, and is selected from 2 {0.01, 0.1, 1}. The parameters are tuned by the smallest E(S) on a sub-graph with 1,000 nodes and 2,000 cascade data for each dataset when = 0.5. Table 2 reports the results. In experiments, we empirically set γ = 0.4, = 0.001, and Lf = 10 6NNc |