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 | Archive PDF | Plain Text | 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. |