Streaming Min-max Hypergraph Partitioning
Authors: Dan Alistarh, Jennifer Iglesias, Milan Vojnovic
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 offline approaches. |
| Researcher Affiliation | Collaboration | Dan Alistarh Microsoft Research Cambridge, United Kingdom dan.alistarh@microsoft.com Jennifer Iglesias Carnegie Mellon University Pittsburgh, PA jiglesia@andrew.cmu.edu Milan Vojnovic Microsoft Research Cambridge, United Kingdom milanv@microsoft.com |
| 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 first 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. |