- Graph & Information Network Mining
- (Big) Data Mining & Analysis
- Databases & Knowledge Discovery
- Recommendation Algorithms, Trust & Personalization
- City Science/ Urban Informatics
Manos Papagelis is an Assistant Professor of Electrical Engineering and Computer Science (EECS) at York University, Toronto, Canada. His research falls is in the area of data science, with particular
interest in graph and information network mining, (big) data mining and analysis, databases and knowledge discovery, recommendation algorithms & personalization, city science/urban informatics. Manos holds a Ph.D. in Computer Science from the University of Toronto, Canada, and a M.Sc. and a B.Sc. in Computer Science from the University of Crete, Greece. Before joining York University, he was a
postdoctoral research scholar at the University of California, Berkeley. In the past, he has worked at Yahoo! Labs, Barcelona as a research intern and at the Institute of Computer Science, FORTH, Greece as a research fellow. His research has appeared in ACM trans. on knowledge discovery from data (ACM TKDD) and IEEE Trans. on knowledge and data engineering (IEEE TKDE), he has filed three U.S.
patent applications and is the software architect of two large-scale online systems – a conference management system and a system for socio-technical analysis of green buildings. He has taught at the
University of California, Berkeley and at the University of Toronto, Canada.
- Papagelis, M. (2015). Refining social graph connectivity via
shortcut edge addition. ACM Transactions on Knowledge Discovery from
Data (ACM TKDD), 10(2), 12.
- Papagelis, M., Das, G., & Koudas, N. (2013). Sampling online social
networks. IEEE Transactions on knowledge and data engineering (IEEE
TKDE), 25(3), 662-676.
- Doerr, M., & Papagelis, M. (2007). A method for estimating the
precision of placename matching. IEEE transactions on knowledge and
data engineering (IEEE TKDE), 19(8), 1089-1101.