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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Streaming Min-max Hypergraph Partitioning
Authors: Dan Alistarh, Jennifer Iglesias, Milan Vojnovic
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 14,221 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,848 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also report results of an extensive empirical evaluation, which demonstrate that this greedy strategy yields superior performance when compared with alternative approaches.Further, we provide experimental evidence that this greedy online algorithm exhibits good performance for several real-world input bipartite graphs, outperforming more complex assignment strategies, and even some of๏ฌine approaches. |
| Researcher Affiliation | Collaboration | Dan Alistarh Microsoft Research Cambridge, United Kingdom EMAIL Jennifer Iglesias Carnegie Mellon University Pittsburgh, PA EMAIL Milan Vojnovic Microsoft Research Cambridge, United Kingdom EMAIL |
| Pseudocode | Yes | Algorithm 1: The greedy algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code or a link to a repository. |
| Open Datasets | Yes | We ๏ฌrst consider a set of real-world bipartite graph instances with a summary provided in Table 3. All these datasets are available online, except for Zune podcast subscriptions. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. It operates in a streaming model. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We allow a slack (parameter c) of up to 100 topics. |