NukCP: An Improved Local Search Algorithm for Maximum k-Club Problem

Authors: Jiejiang Chen, Yiyuan Wang, Shaowei Cai, Minghao Yin, Yupeng Zhou, Jieyu Wu10146-10155

AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Experiments on a broad range of different scale instances show that Nuk CP significantly outperforms the state-of-the-art Mk CP algorithms on most instances in terms of solution quality.
Researcher Affiliation Academia 1School of Computer Science and Information Technology, Northeast Normal University, China 2State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China 3Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun, China 4School of Computer Science and Technology, University of Chinese Academy of Sciences, China
Pseudocode Yes Algorithm 1: the DRM function; Algorithm 2: the Nuk CP algorithm; Algorithm 3: Club Search(G,S)
Open Source Code Yes The detailed results as well as the source code of our Nuk CP can be found in the supplementary material6. https://github.com/yiyuanwang1988/Nuk CP.git
Open Datasets Yes We use the same generator method as (Moradi and Balasundaram 2018) to randomly generate 90 instances for k = 2,3,4. [...] As for the DIMACS benchmark5, we collect all DIMACS instances used in the previous literature for the Mk CP. [...] We consider massive real-world graphs from the Network Data Repository (Rossi and Ahmed 2015)...
Dataset Splits No The paper mentions generating random instances and using benchmarks like DIMACS, but it does not specify explicit train/validation/test dataset splits (e.g., percentages or sample counts).
Hardware Specification Yes All experiments are run on Intel Xeon E5-2640 v4 @ 2.40GHz CPU with 128GB RAM under Cent OS 7.5.
Software Dependencies Yes CPLEX 12.633 and Gurobi 4 are used in the m S/B and ITDBC, respectively.
Experiment Setup Yes The parameters step Max and α in Nuk CP are set to 100 and 0.6 according to our preliminary experiment.