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..
Dynamic Spectral Graph Anomaly Detection
Authors: Jianbo Zheng, Chao Yang, Tairui Zhang, Longbing Cao, Bin Jiang, Xuhui Fan, Xiao-ming Wu, Xianxun Zhu
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on four datasets substantiate the efficacy of our DSGAD method, surpassing state-of-the-art methods on both homogeneous and heterogeneous graphs. |
| Researcher Affiliation | Academia | 1College of Computer Science and Electronic Engineering, Hunan University, China 2School of Computing, Macquarie University, Austrilia 3School of Computer Science and Engineering, Sun Yat-sen University, China 4School of Communication and Information Engineering, Shanghai University |
| Pseudocode | No | The paper describes the methodology using textual explanations and figures (e.g., Figure 1, Figure 2), but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/IWant Be/Dynamic-Spectral Graph-Anomaly-Detection |
| Open Datasets | Yes | Our experiments use four datasets: T-finance (Tang et al. 2022), Tolokers (Likhobaba, Pavlichenko, and Ustalov 2023), Yelp Chi (Rayana and Akoglu 2015), and Amazon (Mc Auley and Leskovec 2013), as detailed in Table 1. |
| Dataset Splits | Yes | In this paper, to ensure fairness, the ratio of training set/validation set/test set for all methods is fixed at 0.4/ 0.3/ 0.3. |
| Hardware Specification | Yes | All methods are executed on a cloud server virtual machine equipped with 8 v CPUs (32G RAM) and one NVIDIA T4 Tensor Core GPU. |
| Software Dependencies | Yes | Our method leverages the Deep Graph Library (DGL 2.0.0) within Py Torch 2.2.1 with Cuda 11.8. |
| Experiment Setup | Yes | All methods are trained using the Adam optimizer with a learning rate of 0.01 for 100 epochs. Each method is executed 10 times, with the model s performance evaluated based on the mean and standard deviation of the evaluation metrics at the 100-th epoch. The parameter C, crucial for determining the number of wavelets, is set to 2. Hidden layers in all methods are set to 64 dimensions. The Conv1D layer has a convolutional kernel size of 3 and a stride of 1. |