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..
Diffusion Maps for Textual Network Embedding
Authors: Xinyuan Zhang, Yitong Li, Dinghan Shen, Lawrence Carin
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed approach outperforms state-of-the-art methods on the vertex-classification and link-prediction tasks. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering Duke University Durham, NC 27707 |
| Pseudocode | No | No explicit pseudocode block or section labeled 'Algorithm' was found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement about open-source code availability nor does it include a link to a code repository. |
| Open Datasets | Yes | We conduct experiments on three real-world datasets: DBLP [28], Cora [15], and Zhihu [26]. |
| Dataset Splits | No | The paper describes training and testing splits for link prediction and multi-label classification but does not explicitly mention a separate 'validation' set or its specific use for hyperparameter tuning. |
| Hardware Specification | Yes | All models are implemented in Tensorflow using a NVIDIA Titan X GPU with 12 GB memory. |
| Software Dependencies | No | The paper mentions 'TensorFlow' but does not specify its version number or any other software dependencies with version details. |
| Experiment Setup | Yes | We set the embedding of dimension d to 200 with ds and dt both equal to 100. The number of hops H is set to 4 and the importance coefficients λh s are tuned for different datasets and different tasks with λ0 > λ1 > > λH. αtt, αss, αts, and αst are set to 1, 1, 0.3 and 0.3 respectively. The number of negative samples K is set to 1 to speed up the training process. |