Copula Mixed-Membership Stochastic Blockmodel
Authors: Xuhui Fan, Richard Yi Da Xu, Longbing Cao
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show its superior performance in capturing group interactions when compared with the baseline models on both synthetic and real world datasets. We analyse three real-world datasets: the NIPS Coauthorship dataset, the MIT Reality Mining dataset [Eagle and (Sandy) Pentland, 2006] and the Lazega-lawfirm dataset [Lazega, 2001]. Table 3: Model Performance (Mean Standard Deviation) on Real-world Datasets. |
| Researcher Affiliation | Academia | Xuhui Fan, Richard Yi Da Xu, Longbing Cao FEIT, University of Technology Sydney, Australia xhfan.ml@gmail.com; {Yida.Xu;Longbing.Cao}@uts.edu.au |
| Pseudocode | No | The paper describes algorithms and inference steps in prose and mathematical notation but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about making its source code openly available or links to a code repository for the methodology described. |
| Open Datasets | Yes | We analyse three real-world datasets: the NIPS Coauthorship dataset, the MIT Reality Mining dataset [Eagle and (Sandy) Pentland, 2006] and the Lazega-lawfirm dataset [Lazega, 2001]. |
| Dataset Splits | No | The paper mentions "Train error" and "Test error" in Table 3, but does not explicitly detail the specific train/validation/test dataset splits (e.g., percentages, sample counts, or the methodology for splitting). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, or memory specifications). |
| Software Dependencies | No | The paper mentions various models (MMSB, i MMM, IRM, LFRM, NMDR) but does not list any specific software dependencies or their version numbers for implementing or running the experiments. |
| Experiment Setup | No | The paper describes the generative model and inference schemes but does not provide specific details about the experimental setup such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or specific training configurations. |