Temporal Networks & Interaction-Driven Modeling

Real-world networks aren’t static — what matters is when interactions happen, not just who is connected. My work shows that future popularity is driven by recent interaction trends rather than current standing, that external shocks to an organization surface as changes in its online interactions, and that the timing of contacts governs how things spread. I use interaction-driven models to analyze contagion — including COVID-19 and airborne transmission in real-world contact networks — and the interplay between an epidemic and the community structure it moves through. It’s a view of collective behavior built on dynamics, not snapshots.

Selected work: the role of trends in growing networks (Mokryn et al., 2016); the interaction-driven temporal transmission model (Marmor et al., 2023); competition among viral strains (Abbey et al., 2022); and size-agnostic change-point detection in evolving networks (Miller & Mokryn, 2020).

References

2023

Sci. Rep.

Assessing Individual Risk and the Latent Transmission of COVID-19 in a Population with an Interaction-Driven Temporal Model

Yanir Marmor, Alex Abbey, Yuval Shahar, and 1 more author

Scientific Reports, 2023

Bib HTML

@article{marmor2023assessing,
  title = {Assessing Individual Risk and the Latent Transmission of COVID-19 in a Population with an Interaction-Driven Temporal Model},
  author = {Marmor, Yanir and Abbey, Alex and Shahar, Yuval and Mokryn, Osnat},
  journal = {Scientific Reports},
  volume = {13},
  number = {1},
  pages = {12955},
  year = {2023},
}

2022

Sci. Rep.

Analysis of the Competition among Viral Strains Using a Temporal Interaction-Driven Contagion Model

Alex Abbey, Yuval Shahar, and Osnat Mokryn

Scientific Reports, 2022

Bib

@article{abbey2022analysis,
  title = {Analysis of the Competition among Viral Strains Using a Temporal Interaction-Driven Contagion Model},
  author = {Abbey, Alex and Shahar, Yuval and Mokryn, Osnat},
  journal = {Scientific Reports},
  volume = {12},
  number = {1},
  pages = {9616},
  year = {2022}
}

2020

PLOS ONE

Size-Agnostic Change Point Detection Framework for Evolving Networks

Hadar Miller and Osnat Mokryn

PLOS ONE, 2020

Bib

@article{miller2020size,
  title = {Size-Agnostic Change Point Detection Framework for Evolving Networks},
  author = {Miller, Hadar and Mokryn, Osnat},
  journal = {PLOS ONE},
  volume = {15},
  number = {4},
  pages = {e0231035},
  year = {2020}
}

2016

PLOS ONE

The Role of Trends in Growing Networks

Osnat Mokryn, Allon Wagner, Marcel Blattner, and 2 more authors

PLOS ONE, 2016

Bib

@article{mokryn2016trends,
  title = {The Role of Trends in Growing Networks},
  author = {Mokryn, Osnat and Wagner, Allon and Blattner, Marcel and Ruppin, Eytan and Shavitt, Yuval},
  journal = {PLOS ONE},
  volume = {11},
  number = {8},
  pages = {e0156505},
  year = {2016}
}