Contextual Stochastic Block Models
Authors: Yash Deshpande, Subhabrata Sen, Andrea Montanari, Elchanan Mossel
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Algorithm: We provide a simple, iterative algorithm for inference based on the belief propagation heuristic. For data generated from the model, we empirically demonstrate that the the algorithm achieves the conjectured information-theoretic threshold. |
| Researcher Affiliation | Academia | Yash Deshpande Department of Mathematics, Massachusetts Institute of Technology Andrea Montanari Departments of Electrical Engineering and Statistics, Stanford University Elchanan Mossel Department of Mathematics, Massachusetts Institute of Technology Subhabrata Sen Department of Mathematics, Massachusetts Institute of Technology |
| Pseudocode | No | The algorithm proceeds by computing, in an iterative fashion vertex messages ηt i, mt a for i [n], a [p] and edge messages ηt i j for all pairs (i, j) that are connected in the graph G. Starting from an initialization (ηt0, mt0)t0= 1,0, we update the messages in the following linear fashion: ηt+1 i j = ... (13). This describes the algorithm but is not formatted as pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | Sample AG, B from Pλ,µ with n = 800, p = 1000, d = 5. This indicates the data was simulated/generated, not a publicly available or open dataset accessed via a link or citation. |
| Dataset Splits | No | Sample AG, B from Pλ,µ with n = 800, p = 1000, d = 5. Run BP algorithm for T = 50 iterations... The paper uses simulated data and does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper states 'In our experiments, we perform 100 Monte Carlo runs of the following process', but does not provide specific hardware details (exact GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper describes the algorithm and experiments but does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In our experiments, we perform 100 Monte Carlo runs of the following process: ... 2. Run BP algorithm for T = 50 iterations with random initialization η0 i , η 1 i , m0 a, m 1 a iid N(0, 0.01). |