Our recent Nature Communications paper presents an identity matching framework that leverages shared bursty activity patterns across platforms, enabling robust detection of accounts belonging to the same entity. The method outperforms state-of-the-art structural and temporal models, remains effective under missing data, and succeeds identifying individuals even after long periods of time.