Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel
Authors: Zheng Yu, Xuhui Fan, Marcin Pietrasik, Marek Z. Reformat6704-6711
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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). |