6 papers · 1 filter
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,…
AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection
Hezhe Qiao, Chaoxi Niu, Ling Chen +1
Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years…
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…
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…
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks
Rongrong Ma, Guansong Pang, Ling Chen
Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recur…
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks
Rongrong Ma, Guansong Pang, Ling Chen
One main challenge in imbalanced graph classification is to learn expressive representations of the graphs in under-represented (minority) classes. Existing generic imbalanced lear…