Structure and Dynamics of Information Pathways in On-line Media

Post date: Oct 22, 2012 5:21:05 PM

Manuel Gomez Rodriguez

Diffusion of information, spread of rumors and infectious diseases are all instances of stochastic processes that occur over the edges of an underlying network. Many times networks over which contagions spread are unobserved and need to be inferred from the diffusion data. Moreover, such networks are often dynamic and change over time.

We investigate the problem of inferring static and dynamic networks based on information diffusion data. We assume there is an unobserved network that may change over time, while we observe the results of a dynamic process spreading over the edges of the network. The task then is to infer the edges and the dynamics of the underlying network.

We develop an algorithm that relies on stochastic convex optimization to efficiently solve the static and the dynamic network inference problem. We apply our algorithm to information diffusion among 3.3 million mainstream media and blog sites and experiment with more than 179 million different pieces of information spreading over the network in a one year period. We study the evolution of information pathways in the online media space and find that information pathways for general recurrent topics are more stable across time than for on-going news events. Clusters of news media sites and blogs often emerge and vanish in matter of days for on-going news events. Major events involving civil population, such as the Libyan's civil war or Syria's uprise, lead to an increased amount of information pathways among blogs as well as in the overall increase in the network centrality of blogs and social media sites.

Joint work with Jure Leskovec, David Balduzzi and Bernhard Schoelkopf.