Do you share LinkedIn's vision of creating economic opportunity for every member of the global workforce?
We're launching the LinkedIn Economic Graph Research program to encourage researchers, academics, and data-driven thinkers to propose how they would use data from LinkedIn to generate insights that may ultimately lead to new economic opportunities.
What research or analysis would you propose that has the potential to create economic opportunity using Economic Graph data?
As of June 15, 2017 the Economic Graph Research program call for proposals has closed. Thank you to all of the the researchers who have submitted their ideas.
Over the next several weeks, we will be reviewing research proposals to identify those that best meet the criteria listed below. If you have submitted a proposal, you or one of your colleagues will be contacted by a LinkedIn representative near the start of the upcoming U.S. academic year (sometime in August).
QUESTIONS ABOUT RESEARCH USING ECONOMIC GRAPH DATA?
If you have other questions about using Economic Graph data for research projects that fall outside of the scope of this program, if you would like updates about the EGR program, or if you would like to be notified about the next EGR call for proposals, you may contact the team at EconomicGraphResearchProposal@linkedin.com.
Economic Graph Research Submissions Requirements
What are the basic requirements to submit a proposal to the Economic Graph Research program?:
- To participate you must be 18 years of age.
- Teams of up to 5 individual participants are permitted.
- Proposals should be on behalf of universities, think tanks, non-governmental organizations or other non-profit entities. Proposals on behalf of for-profit organizations and governments will not be considered.
- Proposals must be submitted using the form provided by LinkedIn.
Program Details
Areas and topics
Analytics:
Analytics is the discovery, interpretation, and communication of actionable insights from big data. At LinkedIn, our mission is to drive understanding and impactful decision-making through rigorous use of data. Our analytics is deeply tied to core modules of our ecosystem, including product, marketing, and sales, to name a few. We are looking for research proposals that leverage big data analytics and data science to understand relationships in the economic graph, preferably in the following areas:
Relationship between Career success and access to relationships;
Occupation skill set trends & predictions and how to get them;
Talent supplies and demand gap globally and/or by geo/country/industry;
Relationship between economics and professional mobility/talent migration.
Economics:
LinkedIn aims to be the go-to source for economic research that creates opportunity for every potential member of the global workforce. Within the Economic Graph Research umbrella, we are interested in rigorously investigating economic and labor market phenomena.
Topics of interest include but are not limited to human capital, career pathways, and productivity growth
AI:
We are interested in a variety of artificial intelligence problems at LinkedIn. They are the fundamental building blocks to drive search, discovery and recommendation across the LinkedIn ecosystem. We are looking for research proposals in this broad context, with preferences given to the following areas:
Large-scale machine learning, including large-scale methods for massive graphs/networks, fast online computations of models, deep learning of knowledge graphs, etc
Personalized machine learning, such as content recommendation for each member, job recommendations for each job seeker, member intention modeling, etc
Reinforcement learning, such as developing conversational interfaces, real-time exploration/exploitation, etc
Crowdsourcing for social networks, for social applications that are highly personalized (feed ranking, link recommendation, etc)
Causal inference, for root-cause analysis and real-time problem triaging
Mining semi-structured & unstructured data, for member information standardization, content understanding and recommendation, etc
Mining time-series & spatial data, for trend analysis, member life-cycle modeling, etc
Security & privacy in machine learning, such as detecting and removing fake accounts, preventing member data breach, etc