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..
Randomized Generation of Adversary-aware Fake Knowledge Graphs to Combat Intellectual Property Theft
Authors: Snow Kang, Cristian Molinaro, Andrea Pugliese, V. S. Subrahmanian4155-4163
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the efficacy of our algorithm on 3 diverse real-world datasets, showing that it achieves high levels of deception. (...) We run experiments on 3 knowledge graph datasets showing that CLIQUE-FAKEKG achieves good results in deceiving adversaries. |
| Researcher Affiliation | Academia | Snow Kang,1 Cristian Molinaro,2 Andrea Pugliese,2 V.S. Subrahmanian1 1 Dartmouth College, USA 2 University of Calabria, Italy |
| Pseudocode | Yes | Algorithm 1 NAIVECLIQUE-FAKEKG (...) Algorithm 2 CLIQUECOMPUTATION (...) Algorithm 3 CLIQUE-FAKEKG |
| Open Source Code | No | For duplication purposes, the code, sample KGs, and sample outputs generated may be downloaded from https://dsaildartmouth.github.io/Fake KG.pdf. (Note: This URL points to the paper's PDF, not source code.) |
| Open Datasets | Yes | We used three datasets: Nation4 (Kim, Xie, and Ong 2016), UMLS5 (Kim, Xie, and Ong 2016), and the Microsoft FB15K-237 (FB for short)6 (Toutanova et al. 2015). (...) 4https://github.com/dongwookim-ml/kg-data/tree/master/nation (...) 5https://github.com/dongwookim-ml/kg-data/tree/master/umls (...) 6https://www.microsoft.com/en-us/download/details.aspx?id=52312 |
| Dataset Splits | No | The paper describes the generation of 66 tests, each with 1 original and 9 fake KGs for human evaluation. However, it does not specify traditional train/validation/test dataset splits used for training or evaluating a machine learning model, as the experiment involves human subjects evaluating generated KGs. |
| Hardware Specification | Yes | We implemented the algorithm in Python on a 2.3 GHz Dual-Core Intel Core i5 with 8GB of LPDDR3 RAM, running Mac OS Catalina Version 10.15.6. |
| Software Dependencies | No | We implemented the algorithm in Python on a (...) running Mac OS Catalina Version 10.15.6. (Python version is not specified, and no other key software components with version numbers are provided.) |
| Experiment Setup | Yes | For each of the 3 datasets we extracted 22 original KGs and, for each KG, we computed 9 fake KGs in particular, we computed 3 fake KGs for each of the following ranges of τ: [0, 1/3], [1/3, 2/3], and [2/3, 1]. (...) The set U was derived as follows: first, we randomly picked a subgraph of the original dataset; then, we built new KGs by randomly adding and deleting vertices/edges/labels to the KGs built so far. (...) We used the Jaccard distance function. |