116 citations · 271 across the 5 of their papers we have counts for
5 papers
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
Shuang Zhou, Xiao Huang, Ninghao Liu +3
Graph anomaly detection (GAD) is a vital task since even a few anomalies can pose huge threats to benign users. Recent semi-supervised GAD methods, which can effectively leverage t…
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
Shuang Zhou, Xiao Huang, Ninghao Liu +2
Graph anomaly detection (GAD) is a vital task since even a few anomalies can pose huge threats to benign users. Recent semi-supervised GAD methods, which can effectively leverage t…
Deep Adversarial Domain Adaptation Based on Multi-layer Joint Kernelized Distance
Sitong Mao, Jiaxin Chen, Xiao Shen +1
Domain adaptation refers to the learning scenario that a model learned from the source data is applied on the target data which have the same categories but different distribution.…
Deep Network Embedding for Graph Representation Learning in Signed Networks
Xiao Shen, Fu-Lai Chung
Network embedding has attracted an increasing attention over the past few years. As an effective approach to solve graph mining problems, network embedding aims to learn a low-dime…
Transfer Prototype-based Fuzzy Clustering
Zhaohong Deng, Yizhang Jiang, Fu-Lai Chung +3
The traditional prototype based clustering methods, such as the well-known fuzzy c-mean (FCM) algorithm, usually need sufficient data to find a good clustering partition. If the av…