Consistent estimation of dynamic and multi-layer block models
Authors: Qiuyi Han, Kevin Xu, Edoardo Airoldi
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We verify the sufficient conditions via simulation and demonstrate that they are practical. In addition, we apply the model to two real data sets: a dynamic social network and a multi-layer social network with several types of relations. |
| Researcher Affiliation | Collaboration | Qiuyi Han QIUYIHAN@FAS.HARVARD.EDU Department of Statistics, Harvard University, Cambridge, MA, USA Kevin S. Xu KEVINXU@OUTLOOK.COM Technicolor Research, Los Altos, CA, USA Edoardo M. Airoldi AIROLDI@FAS.HARVARD.EDU Department of Statistics, Harvard University, Cambridge, MA, USA |
| Pseudocode | No | The paper describes algorithms and methods using mathematical equations and textual descriptions, but it does not include any clearly labeled pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We apply our model on the MIT Reality Mining data set (Eagle and Pentland, 2006). We look at another example from a multi-layer network comprising five kinds of self-reported on-line and off-line relationships between the employees of a research department: Facebook, leisure, work, co-authorship, and lunch (AU-CS ML, 2014). |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages, sample counts) for training, validation, or testing. |
| Hardware Specification | No | The paper does not specify any hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper discusses various methods like spectral clustering and variational approximation, but it does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, specific libraries or solvers with versions). |
| Experiment Setup | Yes | We consider a well-studied scenario where we have 128 nodes initialized randomly into 4 classes (Newman and Girvan, 2004). For each layer, the within-class connection probability is 0.0968, and the between-class connection probability is 0.0521. |