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..
Inductive Anomaly Detection on Attributed Networks
Authors: Kaize Ding, Jundong Li, Nitin Agarwal, Huan Liu
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various attributed networks demonstrate the efficacy of the proposed approach. |
| Researcher Affiliation | Academia | Kaize Ding1 , Jundong Li2,3 , Nitin Agarwal4 and Huan Liu1 1Computer Science and Engineering, Arizona State University, USA 2Electrical and Computer Engineering, University of Virginia, USA 3Computer Science & School of Data Science, University of Virginia, USA 4Information Science, University of Arkansas Little Rock, USA EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: The training process of AEGIS |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | In the experiments, we employ three public real-world attributed network datasets (Blog Catalog [Wang et al., 2010], Flickr [Tang and Liu, 2009], and ACM [Tang et al., 2008]) for performance comparison. |
| Dataset Splits | Yes | we sample another 40% data to construct the newly observed attributed (sub)network G for testing and the remaining 10% data is for the validation purpose. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU/GPU models, memory, or cloud computing instances used for experiments. |
| Software Dependencies | No | The paper mentions software components like ELU, ReLU, and Adam optimizer but does not provide specific version numbers for any programming languages, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | We set the learning rate of the reconstruction loss to 0.005. The training epoch of GDN-AE is 200, while the training epoch of Ano-GAN is 50. We set the parameter K to 3 (Blog Caltalog), 2 (Flickr), 3 (ACM). In addition, the number of samples P is set to 0.05 n for each dataset. |