A novel state space reduction algorithm for team formation in social networks

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.

Enhanced Jellyfish Search Optimizer for Collaborative Team Formation in Social Network

“…To ensure that the skills and abilities needed to complete the task are consistent, they also utilize a new swap operator. In 2021 in [31], The author concentrated on a distinct idea, graph reduction, which condenses the massive data to just the experts and the required skills, enabling the quick extraction of experts for collaboration. In 2022 in [32], the slap swarm algorithm (SSA), the owl search algorithm (OSA), the sooty tern optimization algorithm (STOA), the squirrel search algorithm (SqSA), and the crow search algorithm are five metaheuristic methods the author uses to tackle the team formation problem (CSA).…”

Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation燩roblem

“…The author attempted again in early 2019 to solve the TF issue using particle swarm optimization (PSO) using the same old swap operator [23] their goals were to find a joint team of users who could be able to finish the job while keeping communication costs to a minimum among team members. In [24], the author focused on a different concept, graph reduction, which scales the large data to only appropriate skills and the experts, resulting in the real-time extraction of experts for collaboration. Popular and major contributions to team formation (TF) in literature are given in Tab.…”