Showing cs.LGShow all
3 papers · 1 filter
cs.LG2025
Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts
Jongha Lee, Taehyung Kwon, Heechan Moon +1
The problem of predicting node properties (e.g., node classes) in graphs has received significant attention due to its broad range of applications. Graphs from real-world datasets…
cs.LG2025
TiGer: Self-Supervised Purification for Time-evolving Graphs
Hyeonsoo Jo, Jongha Lee, Fanchen Bu +1
Time-evolving graphs, such as social and citation networks, often contain noise that distorts structural and temporal patterns, adversely affecting downstream tasks, such as node c…
cs.LG2024
SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning
Jongha Lee, Sunwoo Kim, Kijung Shin
To detect anomalies in real-world graphs, such as social, email, and financial networks, various approaches have been developed. While they typically assume static input graphs, mo…