9 papers
PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs
Guoguo Ai, Chaoxi Niu, Hui Yan +3
Activities in numerous evolving systems can be represented as dynamic graphs in snapshot form at different time intervals, i.e., discrete-time dynamic graphs (DTDGs). Existing meth…
Normality Calibration in Semi-supervised Graph Anomaly Detection
Guolei Zeng, Hezhe Qiao, Guoguo Ai +2
Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications. Semi-supervised GAD, which assumes a subse…
FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
Huan Wang, Jun Shen, Haoran Li +6
Federated Learning (FL) enables collaborative training of distributed clients while protecting privacy. To enhance generalization capability in FL, prototype-based FL is in the spo…
Domain-Skewed Federated Learning with Feature Decoupling and Calibration
Huan Wang, Jun Shen, Jun Yan +1
Federated learning (FL) allows distributed clients to collaboratively train a global model in a privacy-preserving manner. However, one major challenge is domain skew, where client…
Identifying Good and Bad Neurons for Task-Level Controllable LLMs
Wenjie Li, Guansong Pang, Hezhe Qiao +2
Large Language Models have demonstrated remarkable capabilities on multiple-choice question answering benchmarks, but the complex mechanisms underlying their large-scale neurons re…
Semi-supervised Graph Anomaly Detection via Robust Homophily Learning
Guoguo Ai, Hezhe Qiao, Hui Yan +1
Semi-supervised graph anomaly detection (GAD) utilizes a small set of labeled normal nodes to identify abnormal nodes from a large set of unlabeled nodes in a graph. Current method…