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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Improving Textual Network Learning with Variational Homophilic Embeddings
Authors: Wenlin Wang, Chenyang Tao, Zhe Gan, Guoyin Wang, Liqun Chen, Xinyuan Zhang, Ruiyi Zhang, Qian Yang, Ricardo Henao, Lawrence Carin
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world networks, for multiple tasks, demonstrate that the proposed method consistently achieves superior performance relative to competing state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | 1Duke University, 2Microsoft Dynamics 365 AI Research |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available from https://github.com/Wenlin-Wang/VHE19. |
| Open Datasets | Yes | Datasets Following [40], we consider three widely studied real-world network datasets: CORA [28], HEPTH [25], and ZHIHU1. |
| Dataset Splits | No | The paper describes "various ratios of observed edges are used for training and the rest are used for testing" for link prediction and "% of Labeled Data" for vertex classification, but does not explicitly mention a distinct validation set or its split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models or cloud instance specifications) used for running experiments. |
| Software Dependencies | No | The paper mentions "a linear SVM [14]" but does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The paper states "Details of the experimental setup are found in the SM" but does not provide specific hyperparameter values or system-level training settings in the main text. |