Sinan Aral, Lev Muchnik andArun Sundararajan

ABSTRACT:

“Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users’ longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300–700%, and that homophily explains 50% of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health.”

Facebook Fellowship Program

Every day Facebook confronts the most complex technical problems and we believe that close relationships with the academy will enable us to address many of these problems at a fundamental level and solve them. As part of our ongoing commitment to academic relations, we are pleased to announce the creation of the Facebook Fellowship program to support graduate students in the 2010-2011 school year.

We are interested in a wide range of academic topics, including the following topical areas:

* Internet Economics: auction theory and algorithmic game theory relevant to online advertising auctions.
* Cloud Computing: storage, databases, and optimization for computing in a massively distributed environment.
* Social Computing: models, algorithms and systems around social networks, social media, social search and collaborative environments.
* Data Mining and Machine Learning: learning algorithms, feature generation, and evaluation methods to produce effective online and offline models of behavioral signals.
* Systems: hardware, operating system, runtime, and language support for fast, scalable, efficient data centers.
* Information Retrieval: search algorithms, information extraction, question answering, cross-lingual retrieval and multimedia retrieval

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