In our research, we address various issues pertaining to the broader topic of data science. We are particularly interested in the development of methods for the study of large relational datasets, complex systems analysis, and collections of time-stamped interactions from various disciplines.
The methods highlighted here range from network science tools, data scraping and inference, and disambiguation.
A network approach to expertise retrieval based on path similarity and credit allocation
Journal of Economic Interaction and Coordination - 2021
gambit - An Open Source Name Disambiguation Tool for Version Control Systems
2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) - 2021
The likelihood-ratio test for multi-edge network models
J. Phys. Complex. 2 035012 - 2021
Predicting Sequences of Traversed Nodes in Graphs using Network Models with Multiple Higher Orders
arXiv preprint arXiv:2007.06662 - 2020
HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks
Proceedings of the 2020 SIAM International Conference on Data Mining - 2020
A Gaussian Process-based Self-Organizing Incremental Neural Network
2019 International Joint Conference on Neural Networks (IJCNN) - 2019
Quantifying Triadic Closure in Multi-Edge Social Networks
ACM - 2019
git2net - An Open Source Package to Mine Time-Stamped Collaboration Networks from Large git Repositories
Proceedings of the 16th International Conference on Mining Software Repositories - 2019
From Relational Data to Graphs: Inferring Significant Links Using Generalized Hypergeometric Ensembles
Social Informatics: 9th International Conference, SocInfo 2017, Oxford, UK, September 13-15, 2017, Proceedings, Part II - 2017
Multiplex Network Regression: How do relations drive interactions?
arXiv e-print - 2017