11 citations · 12 across the 3 of their papers we have counts for
5 papers · 1 filter
UniGAD: Unifying Multi-level Graph Anomaly Detection
Yiqing Lin, Jianheng Tang, Chenyi Zi +3
Graph Anomaly Detection (GAD) aims to identify uncommon, deviated, or suspicious objects within graph-structured data. Existing methods generally focus on a single graph object typ…
GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
Jianheng Tang, Fengrui Hua, Ziqi Gao +2
With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a st…
GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
Amit Roy, Juan Shu, Jia Li +4
Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection,…
Data Imputation from the Perspective of Graph Dirichlet Energy
Weiqi Zhang, Guanlue Li, Jianheng Tang +2
Data imputation is a crucial task due to the widespread occurrence of missing data. Many methods adopt a two-step approach: initially crafting a preliminary imputation (the "draft"…
Dirichlet Graph Variational Autoencoder
Jia Li, Tomasyu Yu, Jiajin Li +5
Graph Neural Networks (GNNs) and Variational Autoencoders (VAEs) have been widely used in modeling and generating graphs with latent factors. However, there is no clear explanation…