  Threshold models of cascades in the social sciences and economics explain the
spread of opinion and innovation due to social influence. In threshold cascade
models, fads or innovations spread between agents as determined by their
interactions with other agents and their personal threshold of resistance.
Typically, these models do not account for structure in the timing of
interaction between the units. In this work, we extend a model of social
cascades by Duncan Watts to temporal interaction networks. In our model, we
assume friends and acquaintances influence agents for a certain time into the
future. That is the influence of the past ages and becomes unimportant. Thus,
our modified cascade model has an effective time window of influence. We
explore two types of thresholds -- thresholds to fractions of the neighbors or
absolute numbers. We try our model on six empirical datasets and compare them
with null models.
