Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
General Tensor Spectral Co-clustering for Higher-Order Data
Authors: Tao Wu, Austin R. Benson, David F. Gleich
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use experiments on both synthetic and real-world problems to validate the effectiveness of our method. For the synthetic experiments, we devise a planted cluster model for tensors and show that GTSC has superior performance compared to other state-of-the-art clustering methods in recovering the planted clusters. In real-world tensor data experiments, we find that our GTSC framework identifies stop-words and semantically independent sets in n-gram tensors as well as worldwide and regional airlines and airports in a flight multiplex network. |
| Researcher Affiliation | Academia | Tao Wu Purdue University EMAIL Austin R. Benson Stanford University EMAIL David F. Gleich Purdue University EMAIL |
| Pseudocode | No | The paper describes the algorithm using numbered steps within paragraphs, but it does not contain a structured pseudocode block or a clearly labeled algorithm section. |
| Open Source Code | Yes | Code and data for this paper are available at: https://github.com/wutao27/Gtensor SC |
| Open Datasets | Yes | Data were collected from http://openflights.org/data.html#route. English n-gram data were collected from http://www.ngrams.info/intro.asp and Chinese n-gram data were collected from https://books.google.com/ngrams. |
| Dataset Splits | No | The paper describes the generation of synthetic data and the real-world datasets used, but it does not specify explicit train/validation/test splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, used to implement and run the experiments. |
| Experiment Setup | Yes | For our experiments, we found that high values (e.g., 0.95) of α do not impede convergence. We use α = 0.8 for all our experiments. For all of our experiments, we set the teleportation probability α = 0.8 and the minimum cluster size for splitting min_size = 2. We set the target conductance φ = 0.3. |