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

Jie Yang

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

11 / 11 papers shown

Title
PyGOD: A Python Library for Graph Outlier Detection Kay Liu Yingtong Dou Xueying Ding Xiyang Hu Ruitong Zhang Hao Peng Kai Shu Philip S. Yu AI4TS 62 51 0 26 Apr 2022
Tree Decomposed Graph Neural Network Yu Wang Hanyu Wang 51 70 0 25 Aug 2021
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning Yixin Liu Zhao Li Shirui Pan Chen Gong Chuan Zhou George Karypis 104 302 0 27 Feb 2021
Beyond Low-frequency Information in Graph Convolutional Networks Deyu Bo Xiao Wang C. Shi Huawei Shen GNN 165 586 0 04 Jan 2021
Adaptive Universal Generalized PageRank Graph Neural Network Eli Chien Jianhao Peng Pan Li O. Milenkovic 240 736 0 14 Jun 2020
Non-Local Graph Neural Networks Meng Liu Zhengyang Wang Shuiwang Ji 100 165 0 29 May 2020
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks Wei-Lin Chiang Xuanqing Liu Si Si Yang Li Samy Bengio Cho-Jui Hsieh GNN 142 1,272 0 20 May 2019
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement W. Kool H. V. Hoof Max Welling 109 220 0 14 Mar 2019
Graph Attention Networks Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio Yoshua Bengio GNN 434 20,089 0 30 Oct 2017
Inductive Representation Learning on Large Graphs William L. Hamilton Z. Ying J. Leskovec 460 15,179 0 07 Jun 2017
Categorical Reparameterization with Gumbel-Softmax Eric Jang S. Gu Ben Poole BDL 291 5,360 0 03 Nov 2016