839 citations · 2k across the 14 of their papers we have counts for
6 papers · 1 filter
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
Xiaoxiao Ma, Jia Wu, Shan Xue +5
Anomalies represent rare observations (e.g., data records or events) that deviate significantly from others. Over several decades, research on anomaly mining has received increasin…
Pseudo-Riemannian Graph Convolutional Networks
Bo Xiong, Shichao Zhu, Nico Potyka +3
Graph convolutional networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally studied through the lens of Euclidean geometry.…
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
Ming Jin, Yizhen Zheng, Yuan-Fang Li +3
Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To…
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
Yixin Liu, Zhao Li, Shirui Pan +3
Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recen…
Graph Self-Supervised Learning: A Survey
Yixin Liu, Ming Jin, Shirui Pan +4
Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised learning, resulting in shortcomings including he…
Task-adaptive Neural Process for User Cold-Start Recommendation
Xixun Lin, Jia Wu, Chuan Zhou +3
User cold-start recommendation is a long-standing challenge for recommender systems due to the fact that only a few interactions of cold-start users can be exploited. Recent studie…