All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining

no code implementations • 15 Feb 2024 • Haihong Zhao, Aochuan Chen, Xiangguo Sun, Hong Cheng, Jia Li

In response to this challenge, we propose a novel approach called Graph COordinators for PrEtraining (GCOPE), that harnesses the underlying commonalities across diverse graph datasets to enhance few-shot learning.

Few-Shot Learning

Weakly Supervised Anomaly Detection via Knowledge-Data Alignment

no code implementations • 6 Feb 2024 • Haihong Zhao, Chenyi Zi, Yang Liu, Chen Zhang, Yan Zhou, Jia Li

In this paper, we introduce a novel framework Knowledge-Data Alignment (KDAlign) to integrate rule knowledge, typically summarized by human experts, to supplement the limited labeled data.

Malware Detection Supervised Anomaly Detection +1

SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases

no code implementations • 25 Aug 2023 • Yang Liu, Jiashun Cheng, Haihong Zhao, Tingyang Xu, Peilin Zhao, Fugee Tsung, Jia Li, Yu Rong

Furthermore, we offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states, which is crucial for model generalization.