Achieving Optimal Misclassification Proportion in Stochastic Block Models
Authors: Chao Gao, Zongming Ma, Anderson Y. Zhang, Harrison H. Zhou
JMLR 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The theoretical property is confirmed by simulated examples. In this section we present the performance of the proposed algorithm on simulated datasets. The experiments cover three different scenarios: (1) dense network with communities of equal sizes; (2) dense network with communities of unequal sizes; and (3) sparse network. |
| Researcher Affiliation | Academia | Chao Gao EMAIL University of Chicago Zongming Ma EMAIL University of Pennsylvania Anderson Y. Zhang EMAIL Harrison H. Zhou EMAIL Yale University |
| Pseudocode | Yes | Algorithm 1: A refinement scheme for community detection [...] Algorithm 2: A greedy method for clustering [...] Algorithm 3: A simplified refinement scheme for community detection |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | No | In this section we present the performance of the proposed algorithm on simulated datasets. The paper uses simulated datasets for its numerical results, but does not provide access information (link, DOI, or citation) for these simulated datasets. |
| Dataset Splits | No | The paper describes generating 'simulated datasets' for its experiments but does not specify any training, testing, or validation splits, as is common for data used in empirical studies involving model training. |
| Hardware Specification | No | The 'Numerical results' section (Section 4) describes various simulation settings and the observed performance of the algorithm, but it does not specify any hardware details such as CPU, GPU models, or memory used for these computations. |
| Software Dependencies | No | The paper discusses algorithmic methods and presents numerical simulation results, but it does not list any specific software or library names with version numbers that were used for implementation or analysis. |
| Experiment Setup | Yes | In this setting, we generate networks with 2500 nodes and 10 communities, each of which consists of 250 nodes, and we set Bii = 0.48 for all i and Bij = 0.32 for all i = j. [...] The constant µ in the critical radius definition was set to be 0.5 in all the results reported here. |