Graph anomaly detection datasets. However, the current literature does not comprehensively discuss this problem, nor does it provide realistic datasets that encompass actual graph structures, anomaly labels, and sensitive May 23, 2025 · Graph anomaly detection (GAD) detects anomalous nodes in real-world networks by capturing topological and attributive information. Oct 21, 2024 · When you’re working on graph anomaly detection, one thing’s for sure: the choice of dataset matters. The results consistently demonstrate the superior performance of our method compared to the baselines. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. . Abstract The Fair Graph Anomaly Detection (FairGAD) problem aims to ac-curately detect anomalous nodes in an input graph while avoiding biased predictions against individuals from sensitive subgroups. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contributors and boost further research in this area Sep 22, 2023 · The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while ensuring fairness and avoiding biased predictions against individuals from Oct 1, 2024 · To demonstrate the effectiveness of our method, we extensively evaluate our approach against state-of-the-art graph anomaly detection techniques on real-world graph classification and wireless network datasets. Although a few of benchmark datasets are publicly available, there is a lack of dynamic heterogeneous graph datasets for advanced GAD research. The data you train on determines how well your model generalizes, so you want to make sure Awesome graph anomaly detection techniques built based on deep learning frameworks. ADRepository: Real-world anomaly detection datasets, including tabular data (categorical and numerical data), time series data, graph data, image data, and video data. xlxpgdza oxwwdc uoi lzrtrmy szi pdpmewq tjdcf sdrw zsfuo idcggudl