Effective team formation in collaboration networks using vertex and proficiency similarity measures

Article type: Research Article

Authors: Sorkhi, Maryam | Hashemi, Sattar^{; *}

Affiliations: Department of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran. E-mails: m.sorkhi.iust@gmail.com, s_hashemi@shirazu.ac.ir

Correspondence: [*] Corresponding author. E-mail: s_hashemi@shirazu.ac.ir.

Abstract: Discovering an effective team of experts toward accomplishing the specific task in social networks has been considered in many real projects. The communication and collaboration among the members and the small cardinality of the team are in the opposite direction to success of the projects. In this paper, we show that the type of a similarity function is also impressive. Its importance is revealed on determining what similar or dissimilar experts should be selected or rejected in the process of the assignment. Considering the graph of underlying social network as conceptual social networking websites, we attribute the team formation problem as a vertices similarity environment based on their common neighbors regarding their co-authored papers. Also, the implicit similarities are used with respect to inattention of additional intermediates between any two nodes in the graph. In addition, taking inspiration from human–human interactions, using just the implicit vertex similarities propose a collaborative recommendation that is based on the team formation framework. They can also identify effectors in social networks established by the structural equivalence relation. Thus, they make the algorithm faster on searching for members of the team. Moreover, the proficiency similarity measures of authors are considered as their potential characteristics that measure their skillfulness level and real contribution corresponding to the required skill. The combination of similarity measures in the cost function causes the algorithm to search the more effective team specially in equal situations. The experimental results on DBLP co-authorship graph show the effectiveness of using the new similarity measures in the proposed method.

Keywords: Social networks, team of experts, similarity measures

DOI: 10.3233/AIC-140628

Journal: AI Communications, vol. 28, no. 4, pp. 637-654, 2015