Reconstruction of Missing Data in Social Networks Based on Temporal Patterns of Interactions
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.
Inverse Problems 2011