Leveraging Node Attributes for Incomplete Relational Data
Authors: He Zhao, Lan Du, Wray Buntine
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our models achieve the stateof-the-art link prediction results, especially with highly incomplete relational data. |
| Researcher Affiliation | Academia | 1Faculty of Information Technology, Monash University, Australia. Correspondence to: He Zhao <he.zhao@monash.edu>. |
| Pseudocode | No | The paper describes mathematical models and inference steps through equations and text, but it does not contain a structured pseudocode or algorithm block. |
| Open Source Code | Yes | 1Code available at https://github.com/ ethanhezhao/NARM/ |
| Open Datasets | Yes | Lazega-cowork: This dataset (Lazega, 2001) contains 378 links of the co-work relationship among 71 attorneys... NIPS234... (Zhou, 2015)... Facebook-ego: The original dataset (Mc Auley & Leskovec, 2012)... Citeseer: This dataset2 contains a citation network... 2http://linqs.umiacs.umd.edu/projects/ /projects/lbc/index.html... Cora: This dataset2 contains a citation network... Aminer: The Aminer dataset (Tang et al., 2009)... Cora-hier... extracted from the original Cora dataset3. 3https://people.cs.umass.edu/ mccallum/ data.html |
| Dataset Splits | No | The paper specifies training data proportions (e.g., 'varied the training data from 10% to 90%') and states 'used the remaining in testing', but it does not explicitly define a separate validation dataset split. |
| Hardware Specification | Yes | (all implemented in MATLAB and running on a desktop with 3.40 GHz CPU and 16GB RAM) |
| Software Dependencies | No | The paper states 'all implemented in MATLAB' but does not specify a version number for MATLAB or other key software dependencies. |
| Experiment Setup | Yes | For Sym-NARM and EPM, we set the truncation level large enough for each dataset: Kmax = 50, 100, 256 for Lazega-cowork, Facebook-ego and NIPS234, NIPS12 respectively... Following (Zhou, 2015), we used 3000 MCMC iterations and computed AUC-ROC/PR with the average probability over the last 1500. |