Multi-View Stochastic Block Models
Authors: Vincent Cohen-Addad, Tommaso D’Orsi, Silvio Lattanzi, Rajai Nasser
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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]. |