K-Core Maximization: An Edge Addition Approach
Authors: Zhongxin Zhou, Fan Zhang, Xuemin Lin, Wenjie Zhang, Chen Chen
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on 9 real-life datasets demonstrate the effectiveness and the efficiency of our proposed methods. |
| Researcher Affiliation | Collaboration | Zhongxin Zhou1,2,4 , Fan Zhang1 , Xuemin Lin2,3,4 , Wenjie Zhang3 and Chen Chen2 1Guangzhou University, Guangzhou, China 2East China Normal University, Shanghai, China 3University of New South Wales, Sydney, Australia 4Zhejiang Lab, Hangzhou, China |
| Pseudocode | Yes | The paper includes 'Algorithm 1 Naive EKC' and 'Algorithm 2 EKC', which are clearly labeled algorithm blocks. |
| Open Source Code | No | The paper states 'All programs are implemented in C++.' and provides links to public datasets, but does not provide an explicit statement or link for the source code of their proposed methods. |
| Open Datasets | Yes | Flickr is from http://networkrepository.com/, and the others are from https://snap.stanford.edu/data/. |
| Dataset Splits | No | The paper does not specify explicit training/validation/test dataset splits, as the research focuses on an optimization problem (k-core maximization) on given graphs rather than training a predictive model requiring such splits. |
| Hardware Specification | Yes | All experiments are performed on Intel Xeon 2.20GHz CPU and Linux System. |
| Software Dependencies | No | The paper states 'All programs are implemented in C++.' but does not provide specific version numbers for any software dependencies or libraries used. |
| Experiment Setup | Yes | The paper explicitly states parameter values used in experiments, for instance, 'k=20, b=5' for figures and discussions of varying k and b. |