Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Pivotal Relationship Identification: The K-Truss Minimization Problem
Authors: Weijie Zhu, Mengqi Zhang, Chen Chen, Xiaoyang Wang, Fan Zhang, Xuemin Lin
IJCAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,946 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,353 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments are conducted over real social networks to demonstrate the ef๏ฌciency 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. |