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..
Robust Negative Sampling for Network Embedding
Authors: Mohammadreza Armandpour, Patrick Ding, Jianhua Huang, Xia Hu3191-3198
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | R-NS is scalable to large-scale networks, and we empirically demonstrate the superiority of R-NS over NS for multi-label classification on a variety of real-world networks including social networks and language networks. |
| Researcher Affiliation | Academia | 1Department of Statistics, Texas A&M University 2Department of Computer Science and Engineering, Texas A&M University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code for running experiments and data are available online at https://github.com/delimited0/network embedding. |
| Open Datasets | Yes | The Blog Catalog dataset (Zafarani and Liu 2009) is a friendship network of bloggers on the Blog Catalog website. The Flickr dataset (Huang, Li, and Hu 2017a) is a network of interactions between Flickr users. The Wikipedia dataset (Mahoney 2011) is the word co-occurrence network of the Wikipedia dataset, which used a 2-word window to determine co-occurrence edges. |
| Dataset Splits | No | The paper mentions 'randomly divide the data into train and test splits' and varying the 'proportion of training examples', but does not explicitly describe a separate validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory). |
| Software Dependencies | No | The paper mentions using the 'Lib Linear library' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | The penalty coefficient λ is calculated based on the formula given in the paper. The degree power β is chosen to be 3/4, which is a widespread default in the literature. Exact settings are presented in the supplementary file. |