Site Search
Computer Science


Dr. Ziad Kobti lecturingDr. Ziad Kobti
Dr. Ziad Kobti
Dr. Luis RuedaDr. Luis Rueda
Dr. Luis Rueda
Robin Gras, Ph.D.Dr. Robin Gras
Dr. Robin Gras
Jessica Chen, Ph.D.Dr. Jessica Chen
Dr. Jessica Chen
Xiaobu Yuan, Ph.D.Dr. Xiaobu Yuan
Dr. Xiaobu Yuan
Christie Ezeife, Ph.D.Dr. Christie Ezeife
Dr. Christie Ezeife
Lambton TowerLambton Tower
Lambton Tower
Imran Ahmad, Ph.D.Dr. Imran Ahmad
Dr. Imran Ahmad
Arunita Jaekel, Ph.D.Dr. Arunita Jaekel
Dr. Arunita Jaekel
Dr. Robert KentDr. Robert Kent
Dr. Robert Kent
Windsor WaterfrontWindsor Waterfront Park
Windsor Waterfront Park
Dr. Scott GoodwinDr. Scott Goodwin
Dr. Scott Goodwin
Alioune Ngom, Ph.D.Dr. Alioune Ngom
Dr. Alioune Ngom

A Framework for the Team Formation Problem Using Evolutionary Computation in Dynamic Social Networks

Add this event into your calendar using the iCAL format
  • Wed, 09/12/2018 - 1:00pm - 3:00pm

A Framework for the Team Formation Problem Using Evolutionary Computation in Dynamic Social Networks

PhD. Thesis Proposal by:

Kalyani Selvarajah

Date: Wednesday, September 12th, 2018
Time:   1:00 pm – 3:00 pm
Location: 3105, Lambton Tower

Abstract: In our research, we try to prove that solving the team formation problem (TFP) on dynamic social networks using evolutionary computations would be advantageous to enhance the accuracy and the time complexity while handling dynamic skill sets for successful completion of tasks. In recent years, different strategies have been proposed to tackle the TFP proficiently. However, because of the dynamic and complex nature that these systems have, solving this problem proven to be NP-hard. This is yet an open problem for further investigation.

The existing approaches in TFP focused on trying to explore the basics of static graphs as the study of their dynamics results. Our research will focus on temporal changes of networks to predict the team members with new possible links.  In order to discover the correct links between individuals to form teams for successful outcomes, we formulated following functions: 1) to examine the active individuals on the considered time-stamp 2) to evaluate the skill similarity among others in the network. 3) for adapted topological knowledge of the networks. The combination of these three major approaches for link prediction on TFP would improve the accuracy of our proposing method. At the same time, modeling a knowledge-based evolutionary framework would reduce the computational complexity. As additional variants of TFP, we are planning to investigate the smart placement for the new individuals, replacement of existing individual and knowledge utilization of team members.

Thesis Committee:     
Internal Reader: Dr. Saeed Samet & Dr. Sherif Saad Ahmed         
External Reader: Dr. Kathryn Pfaff
Advisors: Dr. Ziad Kobti & Dr. Mehdi Kargar                     


See More: