When we launched the LinkedIn Economic Graph Challenge in October 2014, our goal was to work with the best researchers across the U.S. to help solve some of the world’s most pressing issues of our time, using LinkedIn data. We have selected 12 finalists to work with us. Each team submitted a compelling proposal to utilize LinkedIn data to create economic opportunity. These teams aim to solve problems as diverse as closing employee skill gaps, achieving municipal economic improvements and relieving inequality in the labor market. Their results could potentially positively impact millions of people. More at http://economicgraphchallenge.linkedin.com

2. 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…

3. 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

4. 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

5. Katherine Heller
Assistant Professor, Statistical Science
Duke University
David Banks
Professor, Statistical Science
Duke University
Sayan Patra
PhD Student, Statistical Science
Duke University
Text mining on dynamic graphs

6. 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.

7. Abhinav Maurya
Data Science Researcher
Carnegie Mellon University
Rahul Telang
Professor
Carnegie Mellon University
Your next big move:
Personalized data-driven career making

8. 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.

9. 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

10. 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.

11. 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

12. 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.

13. Rajlakshmi De
Senior Research Analyst
Federal Reserve Bank of New York
Linking women to opportunity: Evaluating
gender differences in self-promotion
Kaylyn Frazier
Research Program Manager
Google
Kristen M. Altenburger
Statistics Graduate Student
Harvard University

14. 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.

15. Identifying skill gaps: Determining trends in
supply and demand for skills
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

16. 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.

17. David Dunson
Arts and Sciences Distinguished Professor
Dept. of Statistical Science
Duke 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

18. 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.

19. Azadeh Nematzadeh
PhD Student
Indiana University Bloomington
Jaehyuk 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 Ahn
Assistant Professor
School of Informatics and Computing
Indiana University Bloomington

20. 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.

21. Stanislav Sobolevsky
Research 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

22. 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.

23. 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?

24. 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.

25. Bridging the skills gap by
transforming education
Ozan Candogan
Assistant Professor
Fuqua School of Business
Kostas Bimpikis
Assistant Professor
Stanford Graduate School of Business
Kimon Drakopoulos
PhD Candidate
MIT

26. 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.

27. Learn more at
economicgraphchallenge.linkedin.com

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