Centre for Innovation in Computing at Lassonde

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Manos Papagelis

Associate Professor, EECS Department
Member, IC@L
Member, BRAIN Alliance
Member, AI at York University
Member, Data Mining Lab, EECS Department, York University
Member/, Data Visualization and Analytics Training Program (NSERC CREATE DAV)
Member, Dependable Internet of Things Applications (NSERC CREATE DITA)

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2021 – 2022 Research Highlights

Data Mining (Graph Mining / Trajectory Data Mining / NLP / Machine Learning)

Our research interests include data mining, graph mining, large-scale network analysis, information networks, big data systems and knowledge discovery from data. The emphasis of our research is on theoretical foundations, novel models and algorithms that can provide fast and accurate solutions to complex computational problems and can support data-driven decision making in domains as broad as health, transportation, communication, and engineering. The current research focus is on two areas:

  • Trajectory Data Mining: The primary focus of this research is on discovery of network patterns and dynamics through mining trajectory data streams. This describes a special type of trajectory mining task that seeks to efficiently discover pair-wise relationships among moving objects over time. Mining trajectory data streams to find interesting network patterns is of increased research interest due to a broad range of useful applications, including analysis of transportation systems and location-based social networks.
  • Network Representation Learning: Large-scale network mining and analysis is key to revealing the underlying dynamics of networks, not easily observable. The primary focus of this research is on designing efficient representation learning methods for dynamic networks. Representing networks into low-dimensional spaces occurs in an agnostic way (without domain-expertise) and has the potential to improve the performance of many data mining tasks that now need to operate in lower dimensions. Network mining can support a variety of applications in diverse disciplines and has the potential to impact different industries.
  • Natural language Processing (NLP): Our research work in this area focuses on sentiment analysis, which aims to extract subjective information about the polarity of a set of documents, such as online reviews. Our research focus on emotion detection that can inform affective tasks and includes the following.

Research Highlights

  1. Heidari, F. & Papagelis, M. (2020). Evolving network representation learning based on random walks. Elsevier Applied Network Science, Vol 5, No 18, 1-38. (APNS, Special Issue on Machine Learning with Graphs).
  2. Babanejad, N., Agrawal, A., Davoudi, H., An, A., & Papagelis, M. (2020). Affective and Contextual Embedding for Sarcasm Detection. In Proceedings of the 28th International Conf. on Computational Linguistics, pp. 225-243 (COLING).
  3. Pechlivanoglou, T., Alsaeed, M. & Papagelis, M. (2020). MRSweep: Distributed in-memory sweep-line for scalable object intersection problems. In Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics, pp. 324-333 (IEEE DSAA 2020).
  4. Quader, S., Jaramillo, A., Mukhopadhyay, S., Papagelis, M., Litoiu, M., Kalmuk, D., Mierzejewski, P. (2020). Learning-based workload resource optimization for autonomous database management systems. Disclosure No. P202006917.
  5. Zhao, X., Papagelis, M., An, A., Chen, B. X., Liu, J., & Hu, Y. (2020). Elastic bulk synchronous parallel model for distributed deep learning. Disclosure No. P202006939.