Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection
Authors: Hezhe Qiao, Guansong Pang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical results on 10 real-world GAD datasets show that TAM substantially outperforms seven competing models, achieving over 10% increase in AUROC/AUPRC compared to the best contenders on challenging datasets. |
| Researcher Affiliation | Academia | Hezhe Qiao, Guansong Pang School of Computing and Information Systems, Singapore Management University hezheqiao.2022@phdcs.smu.edu.sg, gspang@smu.edu.sg |
| Pseudocode | Yes | The training algorithms of TAM are summarized in Algorithm 1 and Algorithm 2. Algorithm 1 describes the process of NSGT. Algorithm 2 describes the training process of TAM. |
| Open Source Code | Yes | Our code is available at https://github.com/mala-lab/TAM-master/. |
| Open Datasets | Yes | We conduct the experiments on six commonly-used publicly-available real-world GAD datasets from diverse online shopping services and social networks, and citation networks, including Blog Catalog [49], ACM [48], Amazon [10], Facebook [56], Reddit, and Yelp Chi [21]. The first two datasets contain two types of injected anomalies contextual and structural anomalies [8,34] that are nodes with significantly deviated graph structure and node attributes respectively. The other four datasets contain real anomalies. Detailed information about the datasets can be found in App. B. |
| Dataset Splits | No | The paper mentions running experiments multiple times with different random seeds and using datasets for evaluation, but it does not specify explicit train/validation/test splits (e.g., 80/10/10 percentages or specific sample counts for each split) for its experiments. It refers to standard datasets but not their specific splits used. |
| Hardware Specification | Yes | All the experiments are run on an NVIDIA Ge Force RTX 3090 24GB GPU. |
| Software Dependencies | Yes | TAM is implemented in Pytorch 1.6.0 with python 3.7 and all the experiments are run on an NVIDIA Ge Force RTX 3090 24GB GPU. |
| Experiment Setup | Yes | In TAM, each LAMNet is implemented by a two-layer GCN, and its weight parameters are optimized using Adam [19] optimizer with 500 epochs and a learning rate of 1e 5 by default. T = 3 and K = 4 are used for all datasets. Datasets with injected anomalies, such as Blog Catalog and ACM, require strong regularization, so λ = 1 is used by default; whereas λ = 0 is used for the four real-world datasets. |