Authority-based team discovery in social networks
Abstract
Given a social network of experts, we address the problem of discovering a team of experts that collectively holds a set of skills required to complete a given project. Most prior work ranks possible solutions by communication cost, represented by edge weights in the expert network. Our contribution is to take experts authority into account, represented by node weights. We formulate several problems that combine communication cost and authority, we prove that they are NP-hard, and we propose and experimentally evaluate greedy algorithms to solve them.
This work introduces authority-based methods for team discovery in social networks.
Morteza Zihayat
Principal Investigator
Dr. Morteza Zihayat is a Canada Research Chair (CRC) in Human-Centered AI and Associate Professor at Toronto Metropolitan University, Faculty of Engineering and Architectural Science. He also holds appointments as Adjunct Associate Professor at the University of Waterloo (Management Sciences) and IBM Faculty Fellow at IBM Centre for Advanced Studies. He is the Director of the Human-Centered Machine Intelligence Lab.