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…

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

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

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

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.

Abhinav MauryaData Science Researcher

Carnegie Mellon University

Rahul TelangProfessor

Carnegie Mellon University

Your next big move:

Personalized data-driven career making

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.

Jessica JeffersPhD Candidate

Wharton School, University of Pennsylvania

Michael LeePhD Candidate

Wharton School, University of Pennsylvania

Connecting with coworkers:

The value of within-firm networks

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.

Alexander VolfovskyNSF Mathematical Sciences

Postdoctoral Research Fellow Statistics,

Harvard University

Edoardo AiroldiAssociate Professor

Statistics, Harvard University

Effects of social structure

on labor market dynamics

Panos ToulisPhD Student, Google Fellow

Statistics, Harvard University

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.

Rajlakshmi DeSenior Research Analyst

Federal Reserve Bank of New York

Linking women to opportunity: Evaluating

gender differences in self-promotion

Kaylyn FrazierResearch Program Manager

Google

Kristen M. AltenburgerStatistics Graduate Student

Harvard University

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.

Identifying skill gaps: Determining trends in

supply and demand for skills

Frank MacCroryPostdoctoral Associate

MIT Sloan Initiative on the Digital Economy

George WestermanResearch Scientist

MIT Sloan Initiative on the Digital Economy

Parul BatraMBA Candidate

MIT Sloan School of Management

Noel SequeiraMBA Candidate

MIT Sloan School of Management

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.

David DunsonArts and Sciences Distinguished Professor Dept. of Statistical Science

Duke University

Joseph FutomaPhD Student

Dept. of Statistical Science

Duke University

Yan ShangPhD Student

Fuqua School of Business

Duke University

Find and change your position in a

virtual professional world

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.

Azadeh NematzadehPhD Student

Indiana University Bloomington

Jaehyuk ParkPhD Student

Indiana University Bloomington

Forecasting large-scale industrial evolution

Ian WoodPhD Student

Indiana University Bloomington

Yizhi JingPhD Student

Indiana University Bloomington

Yong-Yeol AhnAssistant Professor

School of Informatics and Computing

Indiana University Bloomington

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.

Stanislav SobolevskyResearch Scientist

MIT

Anthony VankyPhD Candidate

MIT

Iva BojicPostdoctoral Fellow

MIT

Urban professional genome

measuring city performance

Lyndsey RolheiserPhD Candidate

MIT

Hongmou ZhangResearch Fellow

MIT

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.

Bobak MoallemiPhD Student

Stanford Graduate School of Business

Ryan ShyuPhD Student

Stanford Graduate School of Business

Inequality of access to productive labor markets:

How big is it and how can it be fixed?

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.

Bridging the skills gap by

transforming education

Ozan CandoganAssistant Professor

Fuqua School of Business

Kostas BimpikisAssistant Professor

Stanford Graduate School of Business

Kimon DrakopoulosPhD Candidate

MIT

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.

Learn more at

economicgraphchallenge.linkedin.com

©2015 LinkedIn Corporation. All Rights Reserved.