Brendan is a first year PhD student at Carnegie Mellon University. He is working with professors Manuel Blum and Luis von Ahn to better understand the flow of information in social networks and how we can change information flow by modifying network structure.

Brendan is also interested in computer graphics and machine learning. He enjoys taking vacations to Germany and talking about math.

AG 1   Info

MPI Audience

English

30 Minutes

Saarbrücken

We are interested in looking at the spread of information in social networks. In this context 'information' has a broad meaning; we might be talking about product usage, the exchange of gossip, or the spread of a disease. I will present my research on a probabilistic model of information flow in networks and explore hardness and approximation results for some of the following scenarios:

Scenario 1:
You have just invented a product but you don’t have a marketing department. Therefore, you are willing to give away k copies of your product. You hope that satisfied individuals will convince their friends to purchase your product, these friends will convince their friends, and so on. Which k individuals should you give your product to in order to maximize the effect of word-of-mouth marketing?

Scenarios 2 and 3:
What if we can make modifications to the structure of the network? In the case of disease spread, for example, we might be able to quarantine individuals and prevent the disease from spreading through these quarantined individuals. Which individuals or locations should we block off in order to minimize the expected impact of a disease?

In a more positive light, suppose we can introduce individuals to each other and create pathways through which ideas can spread. Which pathways are good to introduce in the network?

This work is supported by a National Science Foundation Graduate Fellowship.

Ariel Rebecca Levavi

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