Accelerated Local Anomaly Detection via Resolving Attributed Networks
Authors: Ninghao Liu, Xiao Huang, Xia Hu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 efficiency 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 {nhliu43, xhuang, xiahu}@tamu.edu |
| 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. |