Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection
Authors: GE ZHANG, Zhenyu Yang, Jia Wu, Jian Yang, Shan Xue, Hao Peng, Jianlin Su, Chuan Zhou, Quan Z. Sheng, Leman Akoglu, Charu Aggarwal
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate i GAD on four real-world graph datasets. Extensive experiments demonstrate the superiority of i GAD on the graph-level anomaly detection task.In this section, we conduct a series of experiments to study the performance of i GAD on the graph-level anomaly detection task. |
| Researcher Affiliation | Collaboration | 1Macquarie University 2University of Wollongong 3Beihang University 4Zhuiyi Technology 5AMSS, Chinese Academy of Sciences 6Carnegie Mellon University 7IBM T. J. Watson Research Center |
| Pseudocode | Yes | The pseudocode of i GAD is included in Algorithm 1 in Appendix A.1. |
| Open Source Code | Yes | The code is available at https://github.com/graph-level-anomalies/i GAD. |
| Open Datasets | Yes | SW-610, MOLT-4, PC-3, and MCF-7 are four real-world graph datasets. These datasets are collected from Pub Chem2, which records a tremendous amount of chemical compounds and their anti-cancer activity testing results ( active or inactive ) on different types of cancer cell lines.2https://pubchem.ncbi.nlm.nih.gov |
| Dataset Splits | Yes | We report experimental results under 5-fold cross-validation.Given the training set, we initialize anomalous substructures and other parameters (Line 1 in Algorithm 1) and calculate the proportion of normal and anomalous graphs (Line 2). For each graph, i GAD learns its graph representation (Lines 3 to 15). Based on the graph representation, a MLP equipped with the PMI-based loss function gives predictions to graphs (Lines 16 and 17). |
| Hardware Specification | No | The information is insufficient. The paper does not specify any particular hardware components (e.g., specific GPU or CPU models, memory details, or cloud instance types) used for running the experiments. |
| Software Dependencies | Yes | The adjacency matrix Ai is sparse, and we compute (Ai)l by the sparse matrix multiplication in Py Torch [34]. [34] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Neur IPS, 32, 2019. |
| Experiment Setup | Yes | Information about parameter setting and algorithm implementation can be found in Appendix B.1. The maximum number of hops K in anomalous attribute-aware graph convolution is set as 2. The number of anomalous substructures M is set as 5, and the size of each anomalous substructure n is set as 8. The maximum random walk length L is set as 5. |