Finding Bipartite Components in Hypergraphs
Authors: Peter Macgregor, He Sun
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We theoretically prove the performance of our proposed algorithm, and compare it against the previous state-of-the-art through extensive experimental analysis on both synthetic and real-world datasets. |
| Researcher Affiliation | Academia | Peter Macgregor School of Informatics University of Edinburgh peter.macgregor@ed.ac.uk He Sun School of Informatics University of Edinburgh h.sun@ed.ac.uk |
| Pseudocode | Yes | Algorithm 1: FINDBIPARTITECOMPONENTS Input :Hypergraph H, starting vector f0 Rn, step size ϵ > 0 Output :Sets L and R |
| Open Source Code | Yes | Our code can be downloaded from https://github.com/pmacg/hypergraph-bipartite-components. |
| Open Datasets | Yes | In particular, on the well-known Penn Treebank corpus that contains 49, 208 sentences and over 1 million words... The Penn Treebank dataset is an English-language corpus... [22] and We construct a hypergraph from a subset of the DBLP network consisting of 14, 376 papers published in artificial intelligence and machine learning conferences [12, 32]. |
| Dataset Splits | No | The paper describes the generation of synthetic datasets and the structure of real-world datasets, but it does not specify how these datasets were split into training, validation, or test sets for model training or evaluation in a reproducible manner. |
| Hardware Specification | Yes | The experiments are performed using an Intel(R) Core(TM) i5-8500 CPU @ 3.00GHz processor, with 16 GB RAM. |
| Software Dependencies | Yes | All algorithms are implemented in Python 3.6, using the scipy library for sparse matrix representations and linear programs. |
| Experiment Setup | Yes | We always set the parameter ϵ = 1 for FBC and FBCA, and we set the starting vector f0 Rn for the diffusion to be the eigenvector corresponding to the minimum eigenvalue of JG, where G is the clique reduction of the hypergraph H. |