Methods for analyzing complex networks: community detection, anomaly and change point detection in temporal interactions, and graph similarity and network alignment, working with linkstreams (continuous interaction sequences) and across topological and temporal scales.
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This work characterizes how communities evolve over time: detecting change points in temporal graphs to distinguish lasting regime changes from transient anomalies, tracking community evolution through temporal motifs and benchmarked detection methods, and tracing individual member trajectories as they join, contribute, and move between communities.
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Applying network analysis to social phenomena: team interactions, platform dynamics, echo chamber detection in online media, and the growth and sustainability of large-scale online collectives such as Wikipedia and open-source communities, connecting network structure to questions from media studies, economics, and sociology.
[Read more](/project/computational-social-science)
This work aims to use techniques from the analysis of social networks and machine learning to produce Learning Analytics (LA) that will support learners and teachers in changing their practices.
We are particularly interested in how the analysis of social interactions allows us to obtain insights into the dynamics of learning strategies.
[Read more](/project/learning-analytics)