January 1, 2011

Supervised Random Walks: Predicting and Recommending Links in Social Networks

ACM International Conference on Web Search and Data Mining (WSDM)

Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open.

Lars Backstrom, Jure Leskovec
January 1, 2011

Network Bucket Testing

International World Wide Web Conference (WWW)

Bucket testing, also known as A/B testing, is a practice that is widely used by on-line sites with large audiences: in a simple version of the methodology, one evaluates a new feature on the site by e…

Lars Backstrom, Jure Leskovec
October 4, 2010

Finding a needle in Haystack: Facebook’s photo storage

USENIX Symposium on Operating Systems Design and Implementation (OSDI)

This paper describes Haystack, an object storage system optimized for Facebook’s Photos application. Facebook currently stores over 260 billion images, which translates to over 20 petabytes of data. U…

Doug Beaver, Sanjeev Kumar, Harry Li, Jason Sobel, Peter Vajgel
June 6, 2010

Data warehousing and analytics infrastructure at Facebook.

Special Interest Group on Management of Data (SIGMOD)

Scalable analysis on large data sets has been core to the functions of a number of teams at Facebook – both engineering and non-engineering. Apart from ad hoc analysis of data and creation of business intelligence dashboards by analysts across the company, a number of Facebook’s site features are also based on analyzing large data sets.

Ashish Thusoo, Dhruba Borthakur, Raghotham Murthy, Zheng Shao, Namit Jain, Hao Liu, Suresh Antony, Joydeep Sen Sarma
June 1, 2010

Not-so-latent dirichlet allocation: collapsed Gibbs sampling using human judgments

Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)

Probabilistic topic models are a popular tool for the unsupervised analysis of text, providing both a predictive model of future text and a latent topic representation of the corpus. Recent studies have found that while there are suggestive connections between topic models and the way humans interpret data, these two often disagree.

Jonathan Chang
June 1, 2010

Tools for Collecting Speech Corpora via Mechanical Turk

NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk

To rapidly port speech applications to new languages one of the most difficult tasks is the initial collection of sufficient speech corpora.

Ian Lane, Alex Waibel, Matthias Eck, Kay Rottmann
April 26, 2010

Find Me If You Can: Improving Geographical Prediction with Social and Spatial Proximity

International World Wide Web Conference (WWW)

Geography and social relationships are inextricably intertwined; the people we interact with on a daily basis almost always live near us. As people spend more time online, data regarding these two dimensions — geography and social relationships — are becoming increasingly precise, allowing us to build reliable models to describe their interaction. These models have important implications in the design of location-based services, security intrusion detection, and social media supporting local communities.

Lars Backstrom, Eric Sun, Cameron Marlow
April 19, 2010

ePluribus: Ethnicity on Social Networks

AAAI CONFERENCE ON WEBLOGS AND SOCIAL MEDIA (ICWSM)

We propose an approach to determine the ethnic break-down of a population based solely on people’s names and data provided by the U.S. Census Bureau. We demonstrate that our approach is able to predict the ethnicities of individuals as well as the ethnicity of an entire population better than natural alternatives.

Jonathan Chang, Itamar Rosenn, Lars Backstrom, Cameron Marlow
April 13, 2010

Job Scheduling for Multi-User MapReduce Clusters

ACM European Conference on Computer Systems (EUROSYS)

Sharing a MapReduce cluster between users is attractive because it enables statistical multiplexing (lowering costs) and allows users to share a common large data set. However, we find that traditiona…

Matei Zaharia, Dhruba Borthakur, Joydeep Sen Sarma, Khaled Elmeleegy, Scott Shenker, Ion Stoica
February 1, 2010

Social Network Activity and Social Well-Being

ACM Conference on Human Factors in Computing Systems (CHI)

Previous research has shown a relationship between use of social networking sites and feelings of social capital. However, most studies have relied on self-reports by college students. The goals of the current study are to (1) validate the common self-report scale using empirical data from Facebook, (2) test whether previous findings generalize to older and international populations, and (3) delve into the specific activities linked to feelings of social capital and loneliness.

Moira Burke, Cameron Marlow, Thomas Lento
February 1, 2010

An Unobtrusive Behavioral Model of “Gross National Happiness”

ACM Conference on Human Factors in Computing Systems (CHI)

I analyze the use of emotion words for approximately 100 million Facebook users since September of 2007. “Gross national happiness” is operationalized as a standardized difference between the use of p…

Adam D. I. Kramer
August 1, 2009

Hive – A Warehousing Solution Over a Map-Reduce Framework

International Conference on Very Large Data Bases (VLDB)

The size of data sets being collected and analyzed in the industry for business intelligence is growing rapidly, making traditional warehousing solutions prohibitively expensive. Hadoop is a popular o…

Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Zheng Shao, Prasad Chakka, Suresh Antony, Hao Liu, Pete Wyckoff
June 1, 2009

Feed Me: Motivating Newcomer Contribution in Social Network Sites

ACM Conference on Human Factors in Computing Systems

Social networking sites (SNS) are only as good as the content their users share. Therefore, designers of SNS seek to improve the overall user experience by encouraging members to contribute more content. However, user motivations for contribution in SNS are not well understood. This is particularly true for newcomers, who may not recognize the value of contribution. Using server log data from approximately 140,000 newcomers in Facebook, we predict long-term sharing based on the experiences the newcomers have in their first two weeks. We test four mechanisms: social learning, singling out, feedback, and distribution.

Moira Burke, Cameron Marlow, Thomas Lento
April 1, 2009

Gesundheit! Modeling Contagion through Facebook News Feed

AAAI Conference on Weblogs and Social Media

Whether they are modeling bookmarking behavior in Flickr or cascades of failure in large networks, models of diffusion often start with the assumption that a few nodes start long chain reactions, resulting in large-scale cascades.

Eric Sun, Itamar Rosenn, Cameron Marlow, Thomas Lento