Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs
Authors: Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The article concludes with an overview of the generalization to more than two classes along with extensive simulations on synthetic and real networks corroborating our findings. |
| Researcher Affiliation | Academia | Lorenzo Dall Amico GIPSA-lab, UGA, CNRS, Grenoble INP lorenzo.dall-amico@gipsa-lab.fr Romain Couillet GIPSA-lab, UGA, CNRS, Grenoble INP L2S, Centrale Supélec, University of Paris Saclay Nicolas Tremblay GIPSA-lab, UGA, CNRS, Grenoble INP |
| Pseudocode | Yes | Algorithm 1 Improved Bethe-Hessian Community Detection |
| Open Source Code | Yes | A Python implementation of the proposed algorithm along with codes to reproduce the results of the article are available at lorenzodallamico.github.io/codes. |
| Open Datasets | Yes | Table 1 next provides a comparison of the algorithm performances on real networks, both labelled and unlabelled, confirming the overall superiority of Algorithm 1, quite unlike HcΦ which fails on several examples. [...] Karate [28] [...] Dolphins [29] [...] Polbooks [30] [...] Football [31] [...] Polblogs [23] [...] Unlabelled networks [32] [...] [32] Jure Leskovec and Andrej Krevl. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, June 2014. |
| Dataset Splits | No | The paper describes the datasets used for experiments, including synthetic and real networks, but does not provide specific details on how these datasets were split into training, validation, or test sets for reproducibility. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU or CPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions 'A Python implementation' but does not provide specific version numbers for Python or any other key software libraries or solvers used in the experiments. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size), optimizer settings, or system-level training configurations for the algorithm described. |