| Abstract: |
|
Everywhere we turn these days, we find massive data sets that are appropriately described as networks. In the high tech world, we see the Internet, the World Wide Web, mobile phone networks, a variety of online social networks like Facebook and LinkedIn, and massive online networks of users and products like Netflix and Amazon. In economics, we are increasingly experiencing both the positive and negative effects of a global networked economy. In epidemiology, we find disease spreading over our ever growing social networks, complicated by mutation of the disease agents. In biomedical research, we are beginning to understand the structure of gene regulatory networks, with the prospect of using this understanding to manage many human diseases. In this talk, I will look quite generally at some of the models we are using to describe these networks, processes we are studying on the networks, algorithms we have devised for the networks, and finally, methods to indirectly infer latent network structure from measured data. I'll discuss in some detail two particular applications: (1) very efficient machine learning algorithms for doing collaborative filtering on massive sparse networks of users and products, like the Netflix network; and (2) inference algorithms on cancer genomic data to suggest possible drug targets for certain kinds of cancer.
|
|
;){kind=link}