2 citations · 7 across the 13 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cs.LG2024
Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach
Chaoxi Niu, Guansong Pang, Ling Chen +1
Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but…
cs.LG2024★ 2 cited
Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts
Chaoxi Niu, Hezhe Qiao, Changlu Chen +2
Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. However,…
cs.LG2024
Graph Continual Learning with Debiased Lossless Memory Replay
Chaoxi Niu, Guansong Pang, Ling Chen
Real-life graph data often expands continually, rendering the learning of graph neural networks (GNNs) on static graph data impractical. Graph continual learning (GCL) tackles this…