Understanding Social Interactions via Temporal Network Analysis

PhD Candidate

• Antonio Longa

Abstract of Dissertation

The COVID-19 epidemic has had a significant impact on society, affecting not only physical health but also mental health, social interactions, and the economy. Measures such as lockdowns, travel restrictions, and social distancing have altered the way we live, work, and interact. Digital contact tracing is a valuable tool in managing infectious disease outbreaks and can help avoid severe lockdowns and excessive quarantines.

Our initial research explored the effectiveness of contact tracing apps, but we faced the challenge of accessing actual temporal interaction networks to accurately simulate disease spread. To gain a deeper understanding of the underlying structures of temporal networks, we delved into the study of temporal networks in our second project. Our focus was on developing an effective approach for identifying temporal motifs in interaction networks, and we introduced the concept of egocentric temporal neighborhoods (ETN) and egocentric temporal motifs (ETM). Finally, we proposed a generative model for temporal networks called ETNgen, which takes into account the intrinsic temporal correlations present in real-world temporal networks. The model captures the time-evolving network structure of egocentric temporal neighborhoods (ETN), thus providing a more accurate representation of real-world networks.