Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Column Networks for Collective Classification
Authors: Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate CLN on multiple real-world applications: (a) delay prediction in software projects, (b) Pub Med Diabetes publication classification and (c) film genre classification. In all of these applications, CLN demonstrates a higher accuracy than state-of-the-art rivals. |
| Researcher Affiliation | Academia | Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh Deakin University, Australia EMAIL |
| Pseudocode | No | The paper describes the mathematical formulations of the model but does not include a formal pseudocode block or algorithm. |
| Open Source Code | Yes | Code for our model can be found on Github 2 https://github.com/trangptm/Column_networks |
| Open Datasets | Yes | We use the largest dataset reported in (Choetkiertikul et al. 2015), the JBoss, which contains 8,206 issues.; We used the Pubmed Diabetes dataset consisting of 19,717 scientific publications and 44,338 citation links among them3. Download: http://linqs.umiacs.umd.edu/projects//projects/lbc/; We used the Movie Lens Latest Dataset (Harper and Konstan 2016) which consists of 33,000 movies. |
| Dataset Splits | Yes | Each dataset is divided into 3 separated sets: training, validation and test sets. For hyper-parameter tuning, we search for (i) number of hidden layers: 2, 6, 10, ..., 30, (ii) hidden dimensions, and (iii) optimizers: Adam or RMSprop. The best training setting is chosen by the validation set and the results of the test set are reported. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU, GPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using ReLU in hidden layers and Adam or RMSprop as optimizers but does not specify version numbers for any software or libraries (e.g., Python, TensorFlow, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | For hyper-parameter tuning, we search for (i) number of hidden layers: 2, 6, 10, ..., 30, (ii) hidden dimensions, and (iii) optimizers: Adam or RMSprop. CLN-FNN has 2 hidden layers and the same hidden dimension with CLN-HWN so that the two models have equal number of parameters. |