Group and Graph Joint Sparsity for Linked Data Classification
Authors: Longwen Gao, Shuigeng Zhou
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both theoretical analysis and experimental results on four benchmark datasets show that the joint sparsity model outperforms traditional group sparsity model and graph sparsity model, as well as the latest group-graph sparsity model. We carry out extensive experiments on real datasets, which show that the new joint sparsity outperforms traditional group sparsity and graph sparsity, as well as the latest g2 sparsity. |
| Researcher Affiliation | Academia | Longwen Gao and Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science Fudan University, Shanghai 200433, China {lwgao, sgzhou}@fudan.edu.cn |
| Pseudocode | No | The paper describes the optimization process using mathematical formulations and prose, but it does not include a clearly labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | Four datasets are used: Cora, Twitter, Gene and Protein. The details of these datasets are summarized in Table 1. Cora is a publication dataset (Mccallum et al., 2000)... Gene is a protein interaction network data from KDD cup 2001... Protein is a dataset extracted from the databases Yeast1 and STRING2. 1http://genomics.stanford.edu 2http://string-db.org/ |
| Dataset Splits | Yes | For each dataset, we perform 10-fold cross-validation by separating the dataset into 10 subsets and each time we draw 1 subset out as test samples and then average the performance. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers required to replicate the experiment. |
| Experiment Setup | No | The paper mentions trade-off parameters λ and γ, but it does not provide their specific values or other hyperparameters and training configurations necessary for reproducibility of the experimental setup. |