Sequential Influence Models in Social Networks

The spread of influence among individuals in a social net- work can be naturally modeled in a probabilistic framework, but it is challenging to reason about differences between var- ious models as well as to relate these models to actual so- cial network data. Here we consider two of the most fun- damental definitions of influence, one based on a small set of "snapshot" observations of a social network and the other based on detailed temporal dynamics. The former is partic- ularly useful because large-scale social network data sets are often available only in snapshots or crawls. The latter how- ever provides a more detailed process model of how influence spreads. We study the relationship between these two ways of measuring influence, in particular establishing how to in- fer the more detailed temporal measure from the more readily observable snapshot measure. We validate our analysis using the history of social interactions on Wikipedia; the result is the first large-scale study to exhibit a direct relationship be- tween snapshot and temporal models of social influence.