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.
Explore theme →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.
Explore theme →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.
Explore theme →Designing AI-based teaching assistants and intelligent tutoring systems: how pedagogical decisions such as what to teach and how can be kept separate from language generation, how large language models can be used reliably despite their error rates and domain variability, and how such systems can evolve from answering questions in isolation toward Socratic companions that accompany students throughout a course.
Explore theme →Chaïma Sehili
Tensor Decompositions for Interpretable Machine Learning on Temporal Graphs
Analysing Peer Assessment Interactions and Their Temporal Dynamics Using a Graphlet-Based Method
EC-TEL · 2021
Series Computational Social Science, Franco Angeli Journals & Series : Methods and Applications in Social Networks Analysis. Evidence from Collaborative, governance, historical, and mobility networks · 2021
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