Download - Economic Graph Challenge: LinkedIn
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Introducing the Economic Graph Challenge
In October 2014, LinkedIn put out an open call for proposals asking researchers,
academics, and data-driven thinkers how they would use data from the LinkedIn
Economic Graph to solve some of the challenging economic problems of our times.
Out of hundreds of submissions, these are the eleven teams whose proposals met
our challenge…
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2015 Winning Proposals
• Text Mining on Dynamic Graphs
• Your Next Big Move: Personalized Data-Driven Career Making
• Connecting with Coworkers: The Value of Within-Firm Networks
*Listed in no particular order
• Effects of Social Structure on Labor Market Dynamics
• Linking Women to Opportunity: Evaluating Gender Differences in Self-Promotion
• Identifying Skill Gaps: Determining Trends in Supply and Demand for Skills
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2015 Winning Proposals
• Find and Change Your Position in a Virtual Professional World
• Forecasting Large-Scale Industrial Evolution
• Urban Professional Genome Measuring City Performance
*Listed in no particular order
• Inequality of Access to Productive Labor Markets: How big is it and How Can it be Fixed?
• Bridging the Skills Gap by Transforming Education
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Katherine HellerAssistant Professor, Statistical Science
Duke University
David BanksProfessor, Statistical Science
Duke University
Sayan PatraPhD Student, Statistical Science
Duke University
Text mining on dynamic graphs
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We propose developing new text models that analyze member profiles and
job listings, utilizing network structure to discover relevant content. The
new models use cutting-edge machine learning methods to predict
changes to both text content and the network dynamics.
Our goal is to invent new information technology that improves how
LinkedIn members are matched with job openings and to advise
companies on which skill sets best match their needs.
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Abhinav Maurya Data Science
Researcher Carnegie Mellon
University
Rahul Telang Professor
Carnegie Mellon University
Yournextbigmove:Personalizeddata-drivencareer
making
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We propose building an engine that can recommend the skills most useful
for a LinkedIn member to learn, based on the member’s existing skillset.
Our goal is to help workers realize their true potential by acquiring skills for
the job that they want, thus making them more competitive in the job
market.
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Jessica Jeffers PhD Candidate Wharton School, University of
Pennsylvania
Michael Lee PhD Candidate Wharton School, University of
Pennsylvania
Connecting with coworkers: The value of within-firm networks
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We propose studying within-firm connectivity, e.g. connections between
managers and employees, to determine how network characteristics affect
the social and economic value of a firm.
By quantifying the importance of within-firm connectivity, we can
encourage and empower companies to build their internal LinkedIn
networks.
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Alexander Volfovsky NSF Mathematical Sciences
Postdoctoral Research Fellow Statistics, Harvard University
Edoardo Airoldi Associate Professor
Statistics, Harvard University
Effects of social structure on labor market dynamics
Panos Toulis PhD Student, Google
Fellow Statistics, Harvard
University
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Our research aims to quantify causal mechanisms through which social
structure and interactions can affect workforce mobility, and labor market
dynamics more generally.
We wish to help policy makers understand the dynamics of economic
mobility in the United States. Our results will enable accurate predictions
and can help inform policy interventions.
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Rajlakshmi DeSenior Research Analyst
Federal Reserve Bank of New York
Linking women to opportunity: Evaluating gender differences in self-promotion
Kaylyn FrazierResearch Program Manager
Kristen M. AltenburgerStatistics Graduate Student
Harvard University
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We will use matching techniques to analyze comparable LinkedIn profiles
between men and women and examine differences in self-promotion. We
will then evaluate whether individuals with higher degrees of self-promotion
receive greater job opportunities.
Our goal is to help women maximize career success through LinkedIn.
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Identifyingskillgaps:Determiningtrendsinsupplyanddemandforskills
Frank MacCrory Postdoctoral Associate MIT Sloan Initiative on the Digital
Economy
George Westerman Research Scientist
MIT Sloan Initiative on the Digital Economy
Parul Batra MBA Candidate MIT Sloan School of
Management
Noel Sequeira MBA Candidate
MIT Sloan School of Management
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Although unemployment is dropping, a skills gap exists: employers face
skill shortages and many workers are underemployed. We propose to
develop tools that show skill gaps and workforce mobility issues in different
segments of the economy.
Our goal is to help job seekers, employers, educators and policy makers
understand, in exceptional detail, skill gaps and other challenges and
opportunities in the labor market.
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David DunsonArts and Sciences Distinguished Professor Dept. of Statistical ScienceDuke University
Joseph Futoma PhD Student Dept. of Statistical
Science Duke University
Yan Shang PhD Student Fuqua School of
Business Duke University
Find and change your position in a virtual professional world
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Our goal is to use relational information from the LinkedIn network to
increase transparency and efficiency of both job searching and recruiting.
We propose determining the relative positions of LinkedIn members in a
virtual professional world. Each LinkedIn member is represented by a point
in space. Closeness between members measures professional similarity.
An institute/company/job can be represented by a data cluster of individual
members, capturing complexity and heterogeneity.
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Azadeh Nematzadeh PhD Student Indiana University
BloomingtonJaehyuk Park PhD Student Indiana University
Bloomington
Forecasting large-scale industrial evolution
Ian Wood PhD Student Indiana University
Bloomington
Yizhi Jing PhD Student Indiana University
Bloomington
Yong-Yeol AhnAssistant ProfessorSchool of Informatics and Computing Indiana University Bloomington
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In order to help professionals adapt to an ever-changing economic
landscape, we want to understand the macro-evolution of industries. We
will analyze the flow of professionals between companies to identify
emerging industries and associated skills.
Our goal is to predict large-scale evolutions of industries and emerging
skills, allowing us to forecast economic trends and guide professionals
towards promising future career paths.
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Stanislav SobolevskyResearch Scientist
MIT
Anthony Vanky PhD Candidate MIT
Iva Bojic Postdoctoral Fellow MIT
Urban professional genome measuring city performance
Lyndsey Rolheiser PhD Candidate MIT
Hongmou Zhang Research Fellow MIT
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We propose creating an “economic genome” of cities, companies, and
individuals that aggregates various associated characteristics from the
Economic Graph. The urban genome will provide a measure of a city’s
economic health, as well as lend insight into the migration patterns of
individuals and firms.
The goal of this analysis is to predict city-level economic trends and to gain
an understanding of what contributes to a city’s economic competitiveness.
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Bobak Moallemi PhD Student Stanford Graduate School of
Business
Ryan Shyu PhD Student
Stanford Graduate School of Business
Inequality of access to productive labor markets:How big is it and how can it be fixed?
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We will focus on job-to-job movements and recruiting activity to study
flows of jobs and workers across geography and industries in the United
States, ultimately aiming to quantify the importance of the job-worker
match for economic growth and dynamism.
Our goal is to allow the evaluation of the effect of various public and
private sector programs on labor market fluidity and opportunity. Examples
include tax incentives, social insurance, and career boards.
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Bridging the skills gap bytransforming education
Ozan Candogan Assistant Professor
Fuqua School of Business
Kostas Bimpikis Assistant Professor Stanford Graduate School of
Business
Kimon Drakopoulos PhD Candidate MIT
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We propose a metric that measures the “distance” between skills,
characterizes the mismatch between the supply and demand for skills in
today’s workforce, and enables us to provide concrete and cost-effective
ways to bridge the skills gap and identify economic opportunities for both
employers and prospective employees.
Our goal is to prescribe cost-effective ways to bridge skills gaps through
efficient matching as well as through recommendations to community
colleges and online course offerings.
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Learn more at economicgraphchallenge.linkedin.com
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