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..
Accelerated Local Anomaly Detection via Resolving Attributed Networks
Authors: Ninghao Liu, Xiao Huang, Xia Hu
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, we compare the proposed framework with the state-of-the-art methods on both real and synthetic datasets, and demonstrate its effectiveness and ef๏ฌciency through quantitative evaluation and case studies. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Texas A&M University 2Center for Remote Health Technologies and Systems, Texas A&M Engineering Experiment Station EMAIL |
| Pseudocode | Yes | Algorithm 1: Accelerated Local Anomaly Detection in Attributed Networks |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We employ four real-world datasets: Disney, Books [Muller et al., 2013], Pol Blog [Perozzi et al., 2014] and DBLP [Silva et al., 2012]. |
| Dataset Splits | No | The paper mentions synthetic and real-world datasets, but does not provide specific details on training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | The experiments are run on a Linux machine with 8 Intel i7 CPUs with 3.40GHz. |
| Software Dependencies | No | The multiprocessing module in Python is used for parallel data processing. However, specific version numbers for Python or any other libraries are not provided. |
| Experiment Setup | Yes | Unless otherwise stated, we set pi,i = 0.2 and pi,j = 0.5pi,i/C. ... We set 10% of attributes to be key attributes, and pc,k = 0.3 for key attributes and pc,k = 0.02 for others. ... When implementing ALAD, we set B = 8 for Disney and Books, B = 16 for Polblog and Synthetic, and B = 24 for DBLP. |