Detecting Hidden Patterns and Anomalies in Academic Citation Networks

Dr. Dvora Toledano-Kitai

Network Science, Machine Learning, and Data Mining

Scientific knowledge grows through complex networks of citations between research papers. In my research, I develop machine learning models that analyze these networks to uncover hidden structures, predict future connections, and detect unusual or anomalous behavior. By combining network theory with advanced computational methods, this work helps us better understand how science evolves and how influential or irregular research patterns emerge over time.

Detecting Pseudo-Manipulated Citations in Scientific Literature through Perturbations of the Citation Graph, MDPI – Mathematics

Assessment of citation suitability via an ant colony-inspired algorithm, ScienceDirect
(In Proceedings of the 29th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2025), Procedia Computer Science, pages 525-533, Osaka, Japan, September 2025)

Appraisal of Citation Reliability Using a Gan-Based Approach
(In Proceedings of the 14th International Conference on Data Science, Technology and Applications (DATA 2025), pages 778-785Bilbao, Spain, July 2025. DOI: 10.5220/0013586500003967)