Cascading Behavior and Bursty Dynamics in Computational Models of Social Networks
Jon Kleinberg, Cornell University
Dec 3, 2004

Abstract

Probabilistic models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of scientific and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of ``word of mouth'' in the promotion of new products. Emerging on-line media such as weblogs offer a compelling new setting in which to study such phenomena, since they are characterized by frequent ``topic bursts'' in which a virtual discussion spreads rapidly through the network of individuals who author on-line content.

A natural way to identify highly influential individuals in such network settings is to look for collections of nodes with the power to trigger large outbreaks of cascading behavior. In other words, if we can try to convince a subset of individuals to adopt a new behavior (e.g. to try a new product or take part in an on-line discussion), and the goal is to cause a large cascade of further adoptions, which set of individuals should we target? In this talk, we will consider such problems in several of the most widely studied models in social network analysis, and describe algorithms, obtained in joint work with David Kempe and Eva Tardos, with provable approximation guarantees for the problem of selecting the most influential set of nodes. The development of these algorithms indicates how influential sets must include members from diverse parts of the network, forcing heuristics for this problem to deal, at least implicitly, with the clustering of the underlying population. We conclude by discussing the intriguing relationship between models of influence propagation and the problem of identifying ``bursty'' topics, which is emerging as an important issue for searching on-line information resources including news and weblogs.