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Gould and Fernandez (1989) developed a local brokerage measure that defines brokering roles based on the group membership of the nodes from the incoming and outgoing edges. This paper extends on this brokerage measure to account for weighted edges and introduces the Weighted-Normalized Gould-Fernandez measure (WNGF). The measure is applied to the EUREGIO inter-regional trade dataset that is a complete, weighted, and directed graph, when transformed. The results gained from the WNGF measure are compared to those from two dichotomized networks: a threshold network and a multiscale backbone network. The results show that edge-weights carry important information regarding the network structure and that retaining edge-weight information ensures the heterogeneity and the nuanced understanding of the brokerage roles.
Purpose: The purpose of this paper is to explore possible factors impacting team performance in healthcare, by focusing on information exchange within and across hospitals boundaries. Design/methodology/approach: Through a web-survey and group interv
With its origin in sociology, Social Network Analysis (SNA), quickly emerged and spread to other areas of research, including anthropology, biology, information science, organizational studies, political science, and computer science. Being its objec
Based on an expert systems approach, the issue of community detection can be conceptualized as a clustering model for networks. Building upon this further, community structure can be measured through a clustering coefficient, which is generated from
Network dismantling aims to scratch the network into unconnected fragments by removing an optimal set of nodes and has been widely adopted in many real-world applications such as epidemic control and rumor containment. However, conventional methods o
The information theoretic quantity known as mutual information finds wide use in classification and community detection analyses to compare two classifications of the same set of objects into groups. In the context of classification algorithms, for i