Emergence of the web and cyberspace gave rise to detailed traces of human social activity. This offers great opportunities to analyze and model behaviors of millions of people. For example, we examined ''planetary scale'' dynamics of a full Microsoft Instant Messenger network that contains 240 million people, with more than 255 billion exchanged messages per month, which makes it the largest social network analyzed to date.

In this talk I will outline my past research on two aspects of the dynamics of large real-world networks: dynamics of information diffusion and cascading behavior in networks, and dynamics of the structure of time evolving networks. I will discuss how empirical findings on complex networks drive progress on theoretical models, and how better models drive advances in algorithms and applications. Last, I will outline interesting directions for future research and the field as a whole.