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Generation is better than Modification: Combating High Class Homophily
  Variance in Graph Anomaly Detection

Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection

15 March 2024
Rui Zhang
Dawei Cheng
Xin Liu
Jie Yang
Ouyang Yi
Xian Wu
Yefeng Zheng
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Papers citing "Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection"

4 / 4 papers shown
Title
Rethinking Graph Neural Networks for Anomaly Detection
Rethinking Graph Neural Networks for Anomaly Detection
Jianheng Tang
Jiajin Li
Zi-Chao Gao
Jia Li
67
193
0
31 May 2022
Beyond Low-frequency Information in Graph Convolutional Networks
Beyond Low-frequency Information in Graph Convolutional Networks
Deyu Bo
Xiao Wang
C. Shi
Huawei Shen
GNN
84
445
0
04 Jan 2021
Geom-GCN: Geometric Graph Convolutional Networks
Geom-GCN: Geometric Graph Convolutional Networks
Hongbin Pei
Bingzhen Wei
Kevin Chen-Chuan Chang
Yu Lei
Bo Yang
GNN
167
1,058
0
13 Feb 2020
Contextual Stochastic Block Models
Contextual Stochastic Block Models
Y. Deshpande
Andrea Montanari
Elchanan Mossel
S. Sen
98
131
0
23 Jul 2018
1