CEWIL Research Matters: September 2017

Article #1: Graph mining to characterize competition for employment (2017)

Author 

Toulis, A., Golab, L

Journal 

Proceedings of the 2nd International Workshop on Network Data Analytics

Source 

Work-Integrated Learning Research Portal

Purpose

To explore a novel application of graph analytics to characterize competition in a large job market.

Methodology

Graph mining techniques were used to analyze competition among communities of prospective employees and employers, using Waterloo co-op data.

Key findings

The graph mining methodology proposed was effective in describing the level of competition between co-op job applicants, but also described the high level of competition among employers for top talent.

 

Practitioner's thoughts by

Robert Craig (Manager, Data Analytics, Reporting and Research, University of Waterloo)

What insights did you gain from reading this article that were useful to you?

As someone new to the co-operative education group, this article provided me with a deeper look into the competitive process that brings together our students and employers at Waterloo. I learned that while Waterloo students are applying to roles in dedicated industries like engineering, finance or IT, there is also significant overlap and competition across programs, particularly for the highest-profile roles. The article also outlines how competitive the hiring process can be amongst our employers, with the largest firms dedicating significant resources to attract the top talent. However, many small businesses and start-ups may be missing out when trying to compete head-to-head with these large companies.

How might the results of this work impact how you do your job?

As we begin to define the role of data analytics in co-operative education, Toulis and Golab’s work provides a great example of the methodologies we want to explore going forward. The article has stoked my interest in learning more about the competitive hiring process that is unique to co-op at Waterloo, and how we can further use data to understand the unique relationships between our student and employers.