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A Chemistry-Oriented Cost-Effective Expert Team Formation in Social Networks

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posted on 23.11.2021, 00:37 by Najaflou, Yashar

The growth of social networks in modern information systems has enabled the collaboration of experts at an unprecedented scale. Given a social network and a task consisting of a set of required skills, Team Formation (TF) aims at finding a team of experts who can cover the required skills and can communicate in an effective manner. However, this definition has been interpreted as the problem of finding teams with minimum communication cost which neglects two aspect of team formation in real life. The first is that in reality experts are multi-skilled, hence communication cost cannot be a fixed value and should vary according to the channels employed. The second ignored aspect is disregarding teams with high expertise level who can still satisfy the required communication level.  To tackle above mentioned issues, I introduce a dynamic formof communication for multi-facet relationships and use it to devise a novel approach called Chemistry Oriented Team Formation (ChemoTF) based on two new metrics; Chemistry Level and Expertise Level. Chemistry Level measures scale of communication required by the task andExpertise Level measures the overall expertise among potential teams filtered by Chemistry Level. Moreover, I adopt a personnel cost metric to filter costly teams. The experimental results on the corpus compiled for this purpose suggests that ChemoTF returns communicative and cost-effective teams with the highest expertise level compared to state-of-the-art algorithms. The corpus itself is a valuable output which contains comprehensive scholarly information in the field of computer science.

History

Copyright Date

01/01/2017

Date of Award

01/01/2017

Publisher

Te Herenga Waka—Victoria University of Wellington

Rights License

Author Retains Copyright

Degree Discipline

Computer Science

Degree Grantor

Te Herenga Waka—Victoria University of Wellington

Degree Level

Masters

Degree Name

Master of Science

ANZSRC Type Of Activity code

970108 Expanding Knowledge in the Information and Computing Sciences

Victoria University of Wellington Item Type

Awarded Research Masters Thesis

Language

en_NZ

Victoria University of Wellington School

School of Engineering and Computer Science

Advisors

Bubendorfer, Kris