Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model

1 code implementation • 3 Aug 2023 • Fu Lin, Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Zitong Wang, Haonan Gong

Then, two competing student models trained by normal and abnormal graphs respectively fit graph representations of the teacher model in terms of node-level and graph-level representation perspectives.

Anomaly Detection

Multi-representations Space Separation based Graph-level Anomaly-aware Detection

1 code implementation • 22 Jul 2023 • Fu Lin, Haonan Gong, Mingkang Li, Zitong Wang, Yue Zhang, Xuexiong Luo

The previous works have observed that abnormal graphs mainly show node-level and graph-level anomalies, but these methods equally treat two anomaly forms above in the evaluation of abnormal graphs, which is contrary to the fact that different types of abnormal graph data have different degrees in terms of node-level and graph-level anomalies.

Reinforcement Learning Guided Multi-Objective Exam Paper Generation

1 code implementation • 2 Mar 2023 • Yuhu Shang, Xuexiong Luo, Lihong Wang, Hao Peng, Xiankun Zhang, Yimeng Ren, Kun Liang

To reduce the repetitive and complex work of instructors, exam paper generation (EPG) technique has become a salient topic in the intelligent education field, which targets at generating high-quality exam paper automatically according to instructor-specified assessment criteria.

Knowledge Tracing Paper generation +2