Symmetric Non-negative Latent Factor Models for Undirected Large Networks
Authors: Xin Luo, Ming-Sheng Shang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real networks show that they are able to a) represent the symmetry of the target network rigorously; b) maintain the non-negativity of resulting latent factors; and c) achieve high computational efficiency when performing data analysis tasks as missing data estimation. |
| Researcher Affiliation | Academia | Xin Luo, Ming-Sheng Shang Institute of Green and Intelligence Technology, Chinese Academy of Sciences Chongqing, China {luoxin21, msshang}@cigit.ac.cn |
| Pseudocode | No | The paper contains mathematical formulations and update rules, but no clearly labeled 'Pseudocode' or 'Algorithm' blocks with structured steps. |
| Open Source Code | No | The paper does not provide any concrete links or statements about releasing the source code for the described methodology. |
| Open Datasets | Yes | The experiments are conducted on four datasets, whose details are given in Table II. All datasets are SHi DS matrices from real applications. D1 and D2 are from the STRING database [Szklarczyk et al., 2015], which contains protein interactome weights in various organs. D3 records the temperature data of a steel cylinder, where the surface nodes form an undirected network. D4 records the vibration stiffness data of a piece of a special kind of material, where the material kernels form an undirected network. Both D3 and D4 are from the University of Florida sparse matrix collection [Davis and Hu, 2011]. |
| Dataset Splits | Yes | On all datasets we adopt the 80%-20% train-test settings and five-fold cross-validations: a) each time we select four subsets to train a model, predicting the remaining one subset, and b) we sequentially repeat this process for five times. |
| Hardware Specification | Yes | All experiments are conducted on a tablet with a 3.4 GHz i7 CPU and 16 GB RAM. |
| Software Dependencies | No | The paper states 'The programming language is JAVA SE 7U60.' However, it does not list any specific software libraries, frameworks, or solvers with version numbers beyond the programming language runtime itself, which is insufficient for reproducibility. |
| Experiment Setup | Yes | For a fair comparison, on each dataset we tune the regularization coefficients of each tested model on one fold, and adopt the same value on the remaining four folds. Meanwhile, since an NLF model s performance further relies on the LF dimension d, we have tested d in the [5, 80] interval. The training process of each tested model terminates if a) the number of consumed iterations reaches a preset threshold, i.e., 1,000, and b) the model converges, i.e., the difference in the training error of two consecutive iterations is smaller than 10-5. |