On clustering network-valued data
Authors: Soumendu Sundar Mukherjee, Purnamrita Sarkar, Lizhen Lin
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our methods using both simulated and real data sets, and theoretical justifications are provided in terms of consistency. |
| Researcher Affiliation | Academia | Soumendu Sundar Mukherjee Department of Statistics University of California, Berkeley Berkeley, California 94720, USA soumendu@berkeley.edu Purnamrita Sarkar Department of Statistics and Data Sciences University of Texas, Austin Austin, Texas 78712, USA purna.sarkar@austin.utexas.edu Lizhen Lin Department of Applied and Computational Mathematics and Statistics Univeristy of Notre Dame Notre Dame, Indiana 46556, USA lizhen.lin@nd.edu |
| Pseudocode | Yes | Algorithm 1 Network Clustering based on Graphon Estimates (NCGE) Algorithm 2 Network Clustering based on Log Moments (NCLM) |
| Open Source Code | Yes | Code used in this paper is publicly available at https://github.com/soumendu041/clustering-network-valued-data. |
| Open Datasets | Yes | We cluster about fifty real world networks. We use 11 co-authorship networks between 15,000 researchers from the High Energy Physics corpus of the ar Xiv, 11 co-authorship networks with 21,000 nodes from Citeseer (which had Machine Learning in their abstracts), 17 co-authorship networks (each with about 3000 nodes) from the NIPS conference and finally 10 Facebook ego networks2. ... 2https://snap.stanford.edu/data/egonets-Facebook.html |
| Dataset Splits | No | The paper mentions using simulated and real data for experiments but does not provide specific train/validation/test splits or cross-validation details for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers required to reproduce the experiments. |
| Experiment Setup | No | The paper describes the algorithms and their theoretical properties but does not provide concrete hyperparameter values or system-level training settings for the experiments. |