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..
Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel
Authors: Zheng Yu, Xuhui Fan, Marcin Pietrasik, Marek Z. Reformat6704-6711
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our model on synthetic and real world data. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, University of Alberta 2School of Mathematics & Statistics, University of New South Wales 3Information Technology Institute, University of Social Sciences, Poland |
| Pseudocode | No | The paper describes the model and inference steps in text and mathematical formulas but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | The Coleman dataset (Coleman and others 1964) contains the information about the friendships of boys in an Illinois high-school. It records the three closest friends for each student in the fall of 1957 and spring of 1958. |
| Dataset Splits | No | The paper mentions '80% for training and 20% for testing' but does not specify a separate validation split or the use of cross-validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions the implementation of a Gibbs sampling scheme with Polya Gamma (PG) approach but does not list any specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper specifies data splitting (80% training, 20% testing) and performance metric (AUC), but does not provide concrete hyperparameter values or specific training/sampling settings (e.g., number of Gibbs iterations, initial values for model parameters). |