AAAI Publications, 2010 AAAI Fall Symposium Series

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Building a Job Lanscape from Directional Transition Data
Dominique Perrault-Joncas, Marina Meila, Marc Scott

Last modified: 2010-11-03


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 traversals 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.


Manifold Learning; Graph Embedding; Spectral Clustering; Labor Market Dynamics; National Longitudinal Survey

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