Pivotal Relationship Identification: The K-Truss Minimization Problem
Authors: Weijie Zhu, Mengqi Zhang, Chen Chen, Xiaoyang Wang, Fan Zhang, Xuemin Lin
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments are conducted over real social networks to demonstrate the efficiency and effectiveness of the proposed techniques. |
| Researcher Affiliation | Academia | 1East China Normal University, China 2Zhejiang Gongshang University, China 3Zhejiang Lab, Hangzhou, China 4The University of New South Wales, Australia |
| Pseudocode | Yes | Algorithm 1: Baseline Algorithm ... Algorithm 2: Group based Algorithm |
| Open Source Code | No | The paper does not provide a statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We employ 9 real social networks (i.e., Bitcoin-alpha, Email-Eu-core, Facebook, Brightkite, Gowalla, DBLP, Youtube, Orkut, Live Journal) to evaluate the performance of the proposed methods. The datasets are public available1. [Footnote 1: https://snap.stanford.edu/data/, https://dblp.org/xml/release/] |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | All the experiments are performed on a machine with an Intel Xeon 2.20 GHz CPU and 128 GB memory running Linux. |
| Software Dependencies | No | The paper states 'All the programs are implemented in C++.' but does not provide specific version numbers for the language or any key software libraries or dependencies. |
| Experiment Setup | Yes | We set default k as 10 for 4 datasets (Gowalla, Youtube, Brightkite, DBLP) and set the default k as 20 for 3 datasets (Facebook, Live Journal, Orkut). We set default b as 5 for all datasets. |