Reconstruction of Missing Data in Social Networks Based on Temporal Patterns of Interactions
Abstract
We discuss a mathematical framework based on a self-exciting point
process aimed at analyzing temporal patterns in the series of interaction events
between agents in a social network. We then develop a reconstruction model
that allows one to predict the unknown participants in a portion of those events.
Finally, we apply our results to the Los Angeles gang network.
Authors
Alexey Stomakhin,
Martin Short,
Andrea Bertozzi
Publication
Inverse Problems 2011
© Alexey Stomakhin, All Rights Reserved.