Sequential Influence Models in Social Networks

By Dan Cosley, Dan Huttenlocher, Jon M. Kleinberg, Xiangyang Lan, Siddharth Suri. 2010

88 citations recorded by the source corpus.

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.

Subfields: Communication, Statistical and Nonlinear Physics

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Citations in the corpus

Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior (2016, background): “Keywords user behavior; cohorts; computational social science; Reddit; temporal”
Quantifying Information Overload in Social Media and Its Impact on Social Contagions (2014, methodology): “Two different approaches to estimate such probability have been proposed: ordinal time estimate or snapshot estimate, the former requiring more detailed data (Cosley et al. 2010).”
The Pulse of News in Social Media: Forecasting Popularity (2012, background): “The effect of user influence on information diffusion motivates another set of investigations [14], [15],[16], [5].”
Information diffusion and external influence in networks (2012, background): “A rich set of models has been developed that all try to describe different mechanisms by which the contagion spreads from the infected to an uninfected node [8, 10, 22, 24, 29].”