Network Tight Community Detection
Authors: Jiayi Deng, Xiaodong Yang, Jun Yu, Jun Liu, Zhaiming Shen, Danyang Huang, Huimin Cheng
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The superiority of the proposed method is demonstrated by various synthetic and real experiments. |
| Researcher Affiliation | Academia | 1Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China 2Department of Statistics, Harvard University, Cambridge MA, USA 3School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China 4Department of Mathematics, University of Georgia, Athens GA, USA 5Department of Biostatistics, Boston University, Boston MA, USA. |
| Pseudocode | Yes | Algorithm 1 Tight Community Detection Algorithm (TCD) |
| Open Source Code | No | The paper does not provide explicit links or statements regarding the open-source availability of its code. |
| Open Datasets | Yes | Specifically, we consider four networks with ground truth community labels: (1) football (Girvan & Newman, 2002) with 115 nodes and 12 communities, (2) polbooks (Krebs, 2005) with 105 nodes and 3 communities, (3) polblogs (Adamic & Glance, 2005) with 1222 nodes and 2 communities, (4) Blog Catalog (Zafarani & Liu, 2009) with 5196 nodes and 6 communities. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It describes generating synthetic data for scenarios and using cross-validation for hyperparameter tuning (determining K), but not for standard data splits for model training and evaluation. |
| Hardware Specification | Yes | All numerical experiments were implemented in Python 3.10 on a Linux server consisting of a 2.2 GHz 24-core Intel Xeon E5-2650 v4 CPU and 64GB of RAM memory capacity. |
| Software Dependencies | No | The paper only mentions 'Python 3.10' as the implementation language. It does not list multiple key software components with their versions or specific version numbers for libraries or solvers. |
| Experiment Setup | Yes | The TCD algorithm has four hyperparameters. ... In all of our examples, we set N/N = 0.7, α = 0.1, L = 50. |