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..
Adversarial Attacks on Node Embeddings via Graph Poisoning
Authors: Aleksandar Bojchevski, Stephan Günnemann
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experimental Evaluation |
| Researcher Affiliation | Academia | 1Technical University of Munich, Germany. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data available at https://www.kdd.in.tum.de/node embedding attack. |
| Open Datasets | Yes | We analyze three datasets: Cora (N = 2810, |E| = 15962, Mc Callum et al. (2000); Bojchevski & G unnemann (2018)) and Citeseer (N = 2110, |E| = 7336, Giles et al. (1998)) are citation networks commonly used to benchmark embedding approaches, and Pol Blogs (N = 1222, |E| = 33428, Adamic & Glance (2005)) is a graph of political blogs. |
| Dataset Splits | No | The paper does not explicitly provide specific train/validation/test dataset splits or mention a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | We set Deep Walk s hyperparameters to: T = 5, b = 5, K = 64 and use logistic regression for classification. |