Venus Haghighi
Ph.D. Student
Macquarie University
School of Computing
Email: venus.haghighi@hdr.mq.edu.au
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Biographical Information
Welcome to my homepage.
I am currently a first-year Ph. D. student at the School of Computing, Macquarie University, Sydney, Australia. My Ph.D. research is under the supervision of Prof. Michael Sheng, and Prof. Jian Yang. My Ph.D. research focuses on developing GNN-based fraud detection systems in both static and dynamic settings.
My research interests include, but not limited to, Graph Data Mining, Graph Neural Networks (GNNs), Anomaly Detection, Federated Learning, Internet of Things (IoT), IoT Security, Mobile Cloud Computing, and Offloading.
Recent News
Our survey paper, " Federated Evaluation in Federated Learning ", has been accepted in the 32nd International Joint Conference on Artificial Intelligence (IJCAI), 2023.
Publications
Haghighi, Venus. "From Classic GNNs to Hyper-GNNs for Detecting Camouflaged Malicious Actors." Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. 2023.
Haghighi, Venus, Behnaz Soltani, Adnan Mahmood, Quan Z. Sheng, and Jian Yang. "GCN-based Multi-task Representation Learning for Anomaly Detection in Attributed Networks." arXiv preprint arXiv:2207.03688 (2022).
Soltani, Behnaz, Haghighi, Venus, Adnan Mahmood, Quan Z. Sheng, and Lina Yao. "A Survey on Participant Selection for Federated Learning in Mobile Networks." arXiv preprint arXiv:2207.03681 (2022).
Haghighi, Venus, and Naghmeh S. Moayedian. "An offloading strategy in mobile cloud computing considering energy and delay constraints." IEEE Access 6 (2018): 11849-11861.