Abstract
Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF’s. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts–resulting in the formation of more communicative teams with high expertise levels.
Highlights
Since the beginning of time, the human race has collaborated and coordinated on activities that are deemed impossible for one human to execute independently
This paper presents a novel Team formation (TF) algorithm for expert team formation called Search Space Reduction-Team Formation (SSR-TF) based on two metrics; communication cost and graph reduction, that will become a basis for future TF’s
In order to demonstrate the efficiency of the proposed SSR-TF algorithm was tested on five datasets, i.e., Universiti Malaysia Pahang (UMP), Database Systems & Logic Programming (DBLP), Association for Computing Machinery (ACM), Internet Movie Database (IMDB), and Bibsonomy
Summary
Since the beginning of time, the human race has collaborated and coordinated on activities that are deemed impossible for one human to execute independently. In 2009, Lappas et al tackled team formation and tried to find expert teams that can fulfill all tasks with minimum communication cost. They called TF an NP hard problem because no polynomial-time algorithm has been able to solve it [4, 5]. The summary of all the popular TF algorithms is given, where it can be noted that all the current works were beneficial to an extent These approaches did not reduce the size of the search space required to fulfill a task, failing to scale well on large datasets. The goal of the proposed heuristic algorithm is to find the least communication cost among all team members
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