Sequential influence models in social networks

Abstract

The spread of influence among individuals in a social network can be naturally modeled in a probabilistic framework, but it is challenging to reason about differences between various models as well as to relate these models to actual social network data. Here we consider two of the most fundamental 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 particularly useful because large-scale social network data sets are often available only in snapshots or crawls. The latter however 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 infer 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 between snapshot and temporal models of social influence. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.