Measuring How Appropriate Individuals Are for Specific Jobs in a Network of Collaborators

Abstract

We simulate social networks, where undirected edges are mutual friendships, to find the effect of their structure on the aptness of persons for performing a given job. A job J requires a given set of tasks, and each node (person) n can perform a given set of tasks. If the ego network EG of n cannot perform all tasks for J, then n fails on J. Otherwise, n’s score is computed as a weighted sum of measures of centrality, embeddedness (core number), attribute and degree assortativity of the nodes in EG, the degrees of these nodes, and the performance of these nodes on accuracy, speed, and reliability. Experiments were run on random networks from three models across values for an independent variable controlling the number of edges: Erdős-Renyi (ER), Barabasi-Albert (BA), and Watts-Strogatz (WS). Average values for maximum, average, and minimum node scores for each value of the variable for each model were plotted. For all models, the core-number measure largely accounts for the curves’ shapes. Our core-number measure averages over node n’s core number and the averages of n’s neighbors’ numbers and the smallest of these. For ER networks, scores increase with increasing number of edges as nodes become more embedded. For BA and WS networks, there is an initial decrease, conjectured to depend on a person collaborating with many little-embedded helpers, untested and perhaps not well trusted. Our approach for members’ aptness for jobs preserves the security of a secure community, keeping the calculations within the community.

Keywords: Social network analysis, Selecting doers, Random network models, Security

DOI: 10.54941/ahfe1004764