Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-View Stochastic Block Models
Authors: Vincent Cohen-Addad, Tommaso D’Orsi, Silvio Lattanzi, Rajai Nasser
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we corroborate our results with experimental evaluations. |
| Researcher Affiliation | Collaboration | 1Google Research 2BIDSA, Bocconi. |
| Pseudocode | Yes | Algorithm 1 Community detection for multi-view stochastic block models; Algorithm 2 Second moment rounding |
| Open Source Code | No | The paper does not provide any link or explicit statement about the availability of its source code. |
| Open Datasets | No | Experiments are presented in Section 5. The next figures compares the results on (z, (f1, G1), . . . , (ft, Gt)) (d,ε,k,t)-MV-SBMn (for a wide range of parameters) of the following algorithms: A.1 Louvain s algorithm (Blondel et al., 2008) on the union graph S A.2 Algorithm 1 with Louvain s algorithm applied in place of the estimator of Theorem 3.1 . (Note: synthetic data does not constitute a public dataset unless made available.) |
| Dataset Splits | No | The paper conducts experiments on synthetic data but does not explicitly mention train, validation, or test splits. The parameters for synthetic data generation are described, but not data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments. |
| Software Dependencies | No | The paper mentions 'Louvain s algorithm (Blondel et al., 2008)' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The next figures compares the results on (z, (f1, G1), . . . , (ft, Gt)) (d,ε,k,t)-MV-SBMn (for a wide range of parameters) of the following algorithms: A.1 Louvain s algorithm (Blondel et al., 2008) on the union graph S A.2 Algorithm 1 with Louvain s algorithm applied in place of the estimator of Theorem 3.1. The y-axis measures agreement as defined in Equation (2). Results are averaged over 20 simulations. Figure 1. Fixing t = 10, n = 1000, k = 10, d = 50 and varying ε in [0.5, 1.5]. Figure 2. Fixing t = 10, n = 1000, k = 10, ε = 0.5 and varying d in [50, 150]. |