Community Detection and Link Prediction via Cluster-driven Low-rank Matrix Completion
Authors: Junming Shao, Zhong Zhang, Zhongjing Yu, Jun Wang, Yi Zhao, Qinli Yang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use ten publicly available real-world datasets to evaluate the performance of CLMC, including Karate, Football, Polbooks, Politics-ie, Olympics, Twitter, USAir, Celegans, PB and NS. |
| Researcher Affiliation | Academia | Data Mining Lab, University of Electronic Science and Technology of China {junmshao, qinli.yang}@uestc.edu.cn |
| Pseudocode | Yes | Algorithm 1 Solving Problem (7) |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | We use ten publicly available real-world datasets to evaluate the performance of CLMC, including Karate, Football, Polbooks, Politics-ie, Olympics, Twitter, USAir, Celegans, PB and NS. |
| Dataset Splits | No | For all data sets, existing links are randomly split into: a training set (90%) and a test set (10%). |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions various algorithms and methods but does not provide specific software names with version numbers required for reproduction. |
| Experiment Setup | Yes | For CLMC, α1 takes the values from (0.001, 0.1, 1) and α2 takes the values from (0.1, 1, 10) . For all algorithms, the best results are recorded. In addition, since the performance of Spectral, PIC, FUSE and CLMC depend on the initialization with k-means clustering, we run them for 10 times, and the best results are reported. |