Building a job lanscape from directional transition data

Dominique Perrault-Joncas, Marina Meilǎ, Marc Scott

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The analysis of career paths suffers from a lack of exploratory tools and dynamic models, due in part to the inherent high dimensionality of the problem. Paths may be understood as directed traversais through a graph whose nodes consist of "job types", which we define as industry and occupation pairs. We want to develop tools to understand and detect high-level features of both the labor market and the workers moving through it - career dynamics. To do this, we map the discrete space of jobs into a d-dimensional continuous space; proximity between jobs will mean that they are "close" to each other in a non-negligible subset of career paths. This embedding allows one to visualize the job landscape. Moreover, we can map individual or groups of career paths to this space, extract features of their collective structure, and construct statistical tests comparing groups by means of this mapping.

Original languageEnglish (US)
Title of host publicationManifold Learning and Its Applications - Papers from the AAAI Fall Symposium, Technical Report
PublisherAI Access Foundation
Number of pages8
ISBN (Print)9781577354888
StatePublished - 2010
Event2010 AAAI Fall Symposium - Arlington, VA, United States
Duration: Nov 11 2010Nov 13 2010

Publication series

NameAAAI Fall Symposium - Technical Report


Other2010 AAAI Fall Symposium
Country/TerritoryUnited States
CityArlington, VA

ASJC Scopus subject areas

  • General Engineering


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