Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues
Authors: Soumendu Sundar Mukherjee, Purnamrita Sarkar, Y. X. Rachel Wang, Bowei Yan
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 1-(a), we have generated a network from an SBM with parameters p = 0.4, q = 0.025, and two equal sized blocks of 100 nodes each. We generate 5000 initializations ψ(0) from Beta(α, β) n and map them to a(0) 1. We perform sample BCAVI updates on ψ(0) with known p, q and color the points in the a(0) 1 co-ordinates according the limit points they have converged to. |
| Researcher Affiliation | Academia | Soumendu Sunder Mukherjee Interdisciplinary Statistical Research Unit (ISRU) Indian Statistical Institute, Kolkata Kolkata 700108, India soumendu041@gmail.com Purnamrita Sarkar Department of Statistics and Data Science University of Texas, Austin Austin, TX 78712 purna.sarkar@austin.utexas.edu Y. X. Rachel Wang School of Mathematics and Statistics University of Sydney NSW 2006, Australia rachel.wang@sydney.edu.au Bowei Yan Department of Statistics and Data Science University of Texas, Austin Austin, TX 78712 boweiy@utexas.edu |
| Pseudocode | No | The paper describes updates using mathematical equations (e.g., equations 4, 8, 9, 10), but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing code for the methodology or links to code repositories. |
| Open Datasets | No | The paper states: "In Figure 1-(a), we have generated a network from an SBM with parameters p = 0.4, q = 0.025, and two equal sized blocks of 100 nodes each." As the data is generated, it's not a publicly available dataset in the conventional sense that requires external access information. |
| Dataset Splits | No | The paper describes generating synthetic data and performing simulations, but it does not specify train/validation/test dataset splits, cross-validation, or reference any predefined splits for a dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., libraries, frameworks, or programming language versions) used for the experiments. |
| Experiment Setup | Yes | In Figure 1-(a), we have generated a network from an SBM with parameters p = 0.4, q = 0.025, and two equal sized blocks of 100 nodes each. We generate 5000 initializations ψ(0) from Beta(α, β) n (for four sets of α and β)." and "For each c0, we initialize ψ(0) such that E(ψ(0)) = (1/2 + c0)1C1 + (1/2 c0)1C2 with iid noise. The y-axis shows the average distance between ψ(20) and the true Z from 500 such initializations." Also, "For every choice of p, q, a network of size 400 with two equal sized blocks was generated." |