Bipartite Stochastic Block Models with Tiny Clusters
Authors: Stefan Neumann
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the algorithm on synthetic and on real-world data; the experiments show that the algorithm can find extremely small clusters even in presence of high destructive noise. |
| Researcher Affiliation | Academia | Stefan Neumann University of Vienna Faculty of Computer Science Vienna, Austria stefan.neumann@univie.ac.at |
| Pseudocode | Yes | Algorithm 1 The pcv algorithm Input: G a bipartite m n graph, k, p, q |
| Open Source Code | Yes | We implemented Algorithm 1 in Python. To compute the truncated SVD we used scikit-learn [23]. The source code is available in the supplementary material. |
| Open Datasets | Yes | The source code and the synthetic data are provided in the supplementary materials. and The Book Crossing dataset1 originates from Ziegler et al. [30]. |
| Dataset Splits | No | No explicit statement of dataset splits (e.g., train/validation percentages or counts) was found. The paper mentions generating synthetic data and evaluating on random graphs, but not how a single dataset was partitioned for training, validation, and testing. |
| Hardware Specification | Yes | The experiments were done on a Mac Book Air with a 1.6 GHz Intel Core i5 and 8 GB RAM. |
| Software Dependencies | No | The paper mentions 'Python' and 'scikit-learn [23]' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | When not mentioned otherwise, the parameters were set to n = 1000, k = 8, ℓ= 70, and m = ℓ k (i.e., 1000 vertices on the right, 8 ground-truth clusters on both sides and left-side clusters of size 70). The size of the right-side clusters was set to r = 8. The parameters p and q were set depending on the dataset. |