Projects per year
Personal profile
Biography
Dr Shan (Emma) Xue is a Lecturer in Computing. She joined the School of Computing in January 2023. Dr Xue received her PhD in Software Engineering from the University of Technology Sydney in 2019, and her PhD in Information Management and Information System from Shanghai University in 2018. She worked as a Lecturer at University of Wollongong (2022) and did her Postdoc at Macquarie University, jointly supported by CSIRO Data61 (2019-2022).
She is available to supervise PhD and Master by Research students with backgrounds in graph analytics, graph representation, deep learning and business/web intelligence.
Research interests
Dr Xue is an experienced researcher with expertise in deep learning, adaptive artificial intelligence, data mining and knowledge discovery in the complex cyber environment. She has been working closely within university research groups, CSIRO Data61 and Australian industry end-users. Her research outcomes mainly contribute to social network analytics, pattern mining and applicational intelligence. They are published in CORE A* international conferences, such as NeurIPS, IJCAI, WSDM, ICDM, and high-impact refereed journals, such as Pattern Recognition, The VLDB Journal, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Knowledge and Data Engineering (TKDE) and IEEE Transactions on Industrial Informatics (TII). She has been general sessional chair at IJCNN, and SPC/PC member at top international conferences, including AAAI, IJCAI, WWW, WSDM and KDD.
External positions
Researcher, CSIRO
18 Feb 2019 → 17 Feb 2022
Fingerprint
- 1 Similar Profiles
Collaborations and top research areas from the last five years
-
Next-Gen Banking: Towards Customized and Personalized Banking Solutions through Generative AI Technologies
Beheshti, A., Wu, J., Zhang, X., Qi, Y., Xue, E. & Alinejad Rokny, H.
1/07/24 → 30/06/28
Project: Research
-
MQRAS 24: Enhancing Natural Disaster Response Networks with Advanced Graph-based Rescue Simulation
Xue, E., Beheshti, A., Asadnia, M., Wu, J., Gore, D., Domingo, S. A. & McKnight, B.
1/07/24 → 31/12/25
Project: Research
-
-
Generative Adversarial Network (GAN) Development for Counter-Fraud Training
Beheshti, A., Xue, E., Lotfi, F. & Simpson, M.
1/01/24 → 31/12/24
Project: Research
-
Linking Cognitive Science and Data Science for Enhanced Therapeutic Interventions
Beheshti, A., Afzoon, S., Zhang, X., Xue, E., Wu, J., Liu, G. & McMahon, J.
15/12/23 → 15/12/24
Project: Research
-
BiF-AC: a bidirectional feedback actor-critic framework for UAV-UGV graph-based search and rescue operations
Cao, X., Luo, H., Wang, G., Xue, S., Yang, J., Wu, J. & Beheshti, A., 2025, Advanced Data Mining and Applications: 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, proceedings, part III. Sheng, Q. Z., Dobbie, G., Jiang, J., Zhang, X., Zhang, W. E., Manolopoulos, Y., Wu, J., Mansoor, W. & Ma, C. (eds.). Singapore: Springer, Springer Nature, p. 66-81 16 p. (Lecture Notes in Artificial Intelligence; vol. 15389).Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
-
Multiknowledge and LLM-inspired heterogeneous graph neural network for fake news detection
Xie, B., Ma, X., Shan, X., Beheshti, A., Yang, J., Fan, H. & Wu, J., 2025, In: IEEE Transactions on Computational Social Systems. 12, 2, p. 682-694 13 p.Research output: Contribution to journal › Article › peer-review
1 Citation (Scopus) -
State of the art and potentialities of graph-level learning
Yang, Z., Zhang, G., Wu, J., Yang, J., Sheng, Q. Z., Xue, S., Zhou, C., Aggarwal, C., Peng, H., Hu, W., Hancock, E. & Liò, P., Feb 2025, In: ACM Computing Surveys. 57, 2, p. 1-40 40 p., 28.Research output: Contribution to journal › Article › peer-review
Open Access1 Citation (Scopus) -
A comprehensive survey on community detection with deep learning
Su, X., Xue, S., Liu, F., Wu, J., Yang, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Jin, D., Sheng, Q. Z. & Yu, P. S., Apr 2024, In: IEEE Transactions on Neural Networks and Learning Systems. 35, 4, p. 4682-4702 21 p.Research output: Contribution to journal › Article › peer-review
227 Citations (Scopus) -
A debiased graph clustering approach using dual contrastive learning
Gao, K., Chen, M., Liu, C., Xue, S., Qiu, Z., Ren, T., Jia, X. & Hu, W., 2024, 2024 IEEE International Conference on Web Services IEEE ICWS 2024: proceedings. Chang, R. N., Chang, C. K., Jiang, Z., Yang, J., Jin, Z., Sheng, M., Fan, J., Fletcher, K., He, Q., Ardagna, C., Yang, J., Yin, J., Wang, Z., Beheshti, A., Russo, S., Atukorala, N., Wu, J., Yu, P. S., Ludwig, H., Reiff-Marganiec, S., Zhang, W., Sailer, A., Bena, N., Li, K., Watanabe, Y., Zhao, T., Wang, S., Tu, Z., Wang, Y. & Wei, K. (eds.). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), p. 1198-1205 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review
Prizes
-
Best Student Paper Awards for “A Framework of Transferring Structures Across Large-scale Information Networks”
Xue, Emma (Recipient), Lu, Jie (Recipient), Zhang, Guangquan (Recipient) & Xiong, Li (Recipient), 2018
Prize
-
Best Student Paper Awards for “FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance”
Zhang, Ge (Recipient), Wu, Jia (Recipient), Yang, Jian (Recipient), Beheshti, Amin (Recipient), Xue, Emma (Recipient), Zhou, Chuan (Recipient) & Sheng, Michael (Recipient), 2021
Prize
-
Most Influential IJCAI2020 Paper Award for “Deep learning for community detection: Progress, challenges and opportunities”
Liu, Fanzhen (Recipient), Xue, Emma (Recipient), Wu, Jia (Recipient), Zhou, Chuan (Recipient), Hu, Wenbin (Recipient), Paris, Cecile (Recipient), Nepal, Surya (Recipient), Yang, Jian (Recipient) & Yu, Philip S. (Recipient), 2020
Prize