41 citations · 195 across the 42 of their papers we have counts for
8 papers · 1 filter
Automated Heterogeneous Network learning with Non-Recursive Message Passing
Zhaoqing Li, Maiqi Jiang, Shengyuan Chen +3
Heterogeneous information networks (HINs) can be used to model various real-world systems. As HINs consist of multiple types of nodes, edges, and node features, it is nontrivial to…
Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels
Zhu Wang, Junnan Dong, Shuang Zhou +3
Graph anomaly detection (GAD) is widely applied in many areas, such as financial fraud detection and social spammer detection. Anomalous nodes in the graph not only impact their ow…
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…
Contrastive Knowledge Graph Error Detection
Qinggang Zhang, Junnan Dong, Keyu Duan +3
Knowledge Graph (KG) errors introduce non-negligible noise, severely affecting KG-related downstream tasks. Detecting errors in KGs is challenging since the patterns of errors are…
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…
FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs
Song Wang, Yushun Dong, Xiao Huang +2
Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose t…
MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs
Qiaoyu Tan, Ninghao Liu, Xiao Huang +3
We introduce a novel masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data. Taking insights from self-supervised learning, we randomly mas…
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
Kaixiong Zhou, Xiao Huang, Daochen Zha +4
Graph neural networks (GNNs) integrate deep architectures and topological structure modeling in an effective way. However, the performance of existing GNNs would decrease significa…