UniGAD: Unifying Multi-level Graph Anomaly Detection
Authors: Yiqing Lin, Jianheng Tang, Chenyi Zi, H. Vicky Zhao, Yuan Yao, Jia Li
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments show that Uni GAD outperforms both existing GAD methods specialized for a single task and graph prompt-based approaches for multiple tasks, while also providing robust zero-shot task transferability. All codes can be found at https://github.com/lllyyq1121/Uni GAD. In this section, we conduct experiments to evaluate our Uni GAD with node-level, edge-level, and graph-level tasks by answering the following questions: Q1: How effective is Uni GAD in unifying in multi-level anomaly detection? Q2: Can Uni GAD transfer information across different levels in zero-shot learning? Q3: What are the contributions of the modular design in the Uni GAD model? Q4: How do the time and space efficiencies of Uni GAD compare to those of other methods? |
| Researcher Affiliation | Academia | Yiqing Lin1 , Jianheng Tang2,3, Chenyi Zi3, H.Vicky Zhao1, Yuan Yao2, Jia Li2,3 1Tsinghua University 2Hong Kong University of Science and Technology 3Hong Kong University of Science and Technology (Guangzhou) linyq20@mails.tsinghua.edu.cn, jtangbf@connect.ust.hk, barristanzi666@gmail.com, vzhao@tsinghua.edu.cn, {yuany,jialee}@ust.hk |
| Pseudocode | Yes | B The Pseudocode of MRQSampler Algorithm We give the pseudocode of MRQSampler in Algorithm 1, which illustrates the algorithm for finding the subgraph with the target node that maximizes the Rayleigh Quotient. |
| Open Source Code | Yes | All codes can be found at https://github.com/lllyyq1121/Uni GAD. |
| Open Datasets | Yes | We consider a total of 14 datasets, including both single-graph datasets and multi-graph datasets. 7 single-graph datasets are used to evaluate the performance of unifying node-level and edgelevel tasks: Reddit, Weibo, Amazon, Yelp, Tolokers, and Questions, T-finance from the work [49], which contain node-level anomaly labels. For edge anomaly labels, we generated them according to a specific anomaly probability following the formula P (i,j) anom = avg(P i anom, P j anom). And 7 multi-graph datasets are used to validate the performance of unifying node-level and graph-level tasks, including BM-MN, BM-MS, BM-MT, MUTAG, MNIST0, MNIST1, and T-Group. The first six datasets are from [35], containing both node anomaly labels and graph anomaly labels. Moreover, we release a real-world large-scale social group dataset T-Group, combining the data (graph anomaly labels) in [26]. |
| Dataset Splits | Yes | Table 1: Detailed statistics of the datasets used in our experiments. Dataset Train% # Graphs # Edges # Nodes # Dims Nodesab Edgesab Graphsab Reddit 40% 1 168,016 10,984 64 3.33% 2.72% / Weibo 40% 1 416,368 8,405 400 10.33% 5.71% / Amazon 70% 1 8,847,096 11,944 25 6.87% 2.49% / Yelp 70% 1 7,739,912 45,954 32 14.53% 13.89% / Tolokers 50% 1 530,758 11,758 10 21.82% 33.44% / Questions 50% 1 202,461 48,921 301 2.98% 7.50% / T-Finance 40% 1 21,222,543 39,357 10 4.58% 2.77% / BM-MN 40% 700 40,032 12,911 1 48.91% / 14.29% BM-MS 40% 700 30,238 9,829 1 31.99% / 14.29% BM-MT 40% 700 32,042 10,147 1 34.49% / 14.29% MUTAG 40% 2,951 179,732 88,926 14 4.81% / 34.40% MNIST0 10% 70,000 41,334,380 4,939,668 5 35.46% / 9.86% MNIST1 10% 70,000 41,334,380 4,939,668 5 35.46% / 11.25% T-Group 40% 37,402 93,367,082 11,015,616 10 0.64% / 4.26%. During training, we conduct a grid search to identify the model that achieves the highest AUROC score on the validation set. |
| Hardware Specification | Yes | Our experiments were mainly carried out on a Linux server equipped with dual AMD EPYC 7763 64-core CPU processor, 256GB RAM, and an NVIDIA RTX 4090 GPU with 24GB memory. Some of the extremely large datasets, such as T-Finance, and certain memory-intensive baselines were implemented on the NVIDIA 8*A800 GPUs. |
| Software Dependencies | No | In Uni GAD, we choose two backbone GNN encoders: GCN [24] and BWGNN [50]. We use a shared graph pre-training method, Graph MAE [21], to obtain a more generalized node representation. The hyperparameters for this step are set to the default values from the official Graph MAE implementation, with 50 training epochs. |
| Experiment Setup | Yes | Table 7: Hyperparameters search space for Uni GAD. Hyperparameter Distribution learning rate Range(5 4, 10 2) activation [Re LU, Leaky Re LU, Tanh] hidden dimension [16,32,64] MRQSampler tree depth [1,2] Graph Stitch Network layer [1,2,3] 2 epochs [100, 200, 300, 400, 500] |