Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Adversarial Attacks on Node Embeddings via Graph Poisoning
Authors: Aleksandar Bojchevski, Stephan Günnemann
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,735 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,794 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |