Graph Quality Judgement: A Large Margin Expedition
Authors: Yu-Feng Li, Shao-Bo Wang, Zhi-Hua Zhou
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate that our proposed method can effectively improve the safeness of GSSL, in addition achieve highly competitive accuracy with many state-of-the-art GSSL methods. |
| Researcher Affiliation | Academia | Yu-Feng Li Shao-Bo Wang Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023, China {liyf,wangsb,zhouzh}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 The LEAD Method |
| Open Source Code | No | No explicit statement or link was provided for the open-source code of the LEAD method. External code for comparison methods (e.g., Harmonic, CGL) is mentioned. |
| Open Datasets | Yes | Downloaded from http://olivier.chapelle.cc/ssl-book/benchmarks.html and http://archive.ics.uci.edu/ml/datasets.html |
| Dataset Splits | Yes | For the GSSL-CV method, 5-fold cross-validation is conducted (we have conducted other types of cross-validation method, like 2-fold and 10-fold cross-validation, and 5-fold cross-validation performs the best). For each data set, 10 instances are labeled and the rest are unlabeled. The class ratio is maintained on both sets. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions software like LIBLINEAR and specific parameters for methods (e.g., Harmonic, CGL, LLGC) but does not provide specific version numbers for these software components or other ancillary software dependencies. |
| Experiment Setup | Yes | For the LEAD method, the parameters C1, C2 and β are set to 1, 0.01 and 0.02 for all the experimental settings in this paper. 9 candidate graphs from 3, 5 and 7 nearest neighbor graphs based on 3 distance metrics (i.e., Euclidean, Manhattan and Cosine distance) [Zhu, 2007] are exploited... |