View source for Cosley, D., D. Huttenlocher, J. Kleinberg, X. Lan, and S. Suri. 2010. Sequential Influence Models in Social Networks.

== Online Version ==
An electronic version of this paper can be downloaded here: [http://www.cs.cornell.edu/Info/People/kleinber/icwsm10-seq.pdf]

== Summary ==
This paper examines the sequential influence in social networks based on two types of temporal data: 1) ordinal-time observations and 2) snapshot observations. The former is concerned with the fraction of the k-exposed set that becomes adopters before its (k+1)^st neighbor becomes an adopter, and the latter also investigates the evolution of the k-exposed set but within the interval of two consecutive snapshots. One of the major contributions of this paper is providing a method for simulating ordinal time from snapshots, which works by choosing the number of neighbors each node had when it became an adopter from a hypothesis set so that it's consistent with the snapshot observations. 

== Results ==
The authors applied their analysis to the Wikipedia dataset that consists of the page edit history and user-talk pages (defining the social network) in multiple languages from January 15, 2001 to April 2, 2007. Here are some of their findings on this dataset:

1. For ordinal-time observations, the curve that shows the probability of editing and article on Wikipedia as a function of the number of interactions in the user-talk page has increasing effect for the first five links, and becomes saturate with more subsequent links.

2. For snapshot observations, the same curve steadily increases with more links but with diminishing marginal influence.

[[File:Fig1.png]]

3. A power law is a good approximation for estimating the number of user/community pairs with respect to number of neighbors in community.

[[File:Fig2.png]]

4. The simulation of ordinal time observations from snapshots becomes increasingly accurate with more snapshots, which is not very surprising since in the limit snapshot measurements converge to the ordinal-time measurements.

== Related Papers ==
Gruhl, D.; Liben-Nowell, D.; Guha, R. V.; and Tomkins, A. 2004. Information diffusion through blogspace. In Proc. 13th International World Wide Web Conference.

Kossinets, G., and Watts, D. 2006. Empirical analysis of an evolving social network. Science 311:88–90.

Backstrom, L.; Huttenlocher, D.; Kleinberg, J.; and Lan, X. 2006. Group formation in large social networks: Membership, growth, and evolution. In Proc. 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.