Optimality of variational inference for stochasticblock model with missing links
Authors: Solenne Gaucher, Olga Klopp
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our results with numerical studies of simulated and real networks, which confirm the advantages of this estimator over current methods. (...) Finally, in Section 4 we provide an extensive numerical study both on synthetic and real-life data which shows clear advantages of our estimator over current methods. |
| Researcher Affiliation | Academia | Solenne Gaucher D epartement de Math ematiques d Orsay, Universit e Paris-Saclay, Orsay, France; Olga Klopp ESSEC Business School, Cergy, France CREST, ENSAE, Palaiseau, France |
| Pseudocode | No | The paper mentions that "The expectation maximization (EM) algorithm derived in [48] can be used to iteratively compute the variational estimator. For details, see Appendix C." However, the provided text does not contain a clearly labeled pseudocode or algorithm block within its main sections, and without access to Appendix C, it cannot be confirmed if this paper explicitly presents the pseudocode itself. |
| Open Source Code | No | The paper mentions external packages such as "miss SBM" and "soft Impute" that implement certain methods, but it does not state that the authors are providing open-source code for the methodology described in this paper. |
| Open Datasets | Yes | We apply our algorithm to analyze a network of interactions within a French elementary school collected by the authors of [47]. (...) network of co-authorship between scientists working on network analysis, first analysed in [43]. |
| Dataset Splits | Yes | We use the observations collected on Day 1 estimate the matrix , and evaluate those estimators on the network of interactions corresponding to Day 2. (...) We introduce 50% of missing values in the dataset. We train the three estimators on the observed entries of the adjacency matrix, and we use the unobserved entries to evaluate their imputation error. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper mentions software packages like "miss SBM" and "soft Impute" but does not specify their version numbers or other ancillary software with version information required for reproducibility. |
| Experiment Setup | No | The paper describes general parameters for synthetic data generation (e.g., "three-communities stochastic block model," "50% missing values") and how real-world data was preprocessed (e.g., interaction duration). However, it does not provide specific hyperparameters for the variational inference estimator itself (e.g., learning rates, number of epochs/iterations if applicable), nor detailed system-level training settings for their proposed method. |