Node Centrality in Weighted Networks: Generalizing Degree and Shortest Paths

This 2010 article focuses on Social Network Analysis (SNA) and proposes a new generation of node centrality measures for weighted networks. The authors do this by building on previous research, which uses weights to operationalize tie strength between nodes within networks. The authors argue that current measures of node centrality—degree, closeness, and betweeness—have solely focused on tie weights, and not on the number of ties, which was the central component of the original measures of network analysis. The authors challenge generalizations in network analysis that solely focus on tie weights, and not on the number of ties within a given social network. The authors instead propose new generalizations that combine aspects from both tie weight and node centrality measures for weighted networks.

This article will be of use to researchers and practitioners interested in Social Network Analysis (SNA) methods within weighted networks. It will be of particular use to social networks scholars, researchers and practitioners looking to expand node centrality measures beyond current approaches, and capture more complex relational states between nodes. The second generation of measures proposed by the authors takes into consideration both the weight of ties and the number of ties, where the relative importance of these two aspects are controlled by a tuning parameter. The paper then illustrates the benefits of this approach by applying it to Freeman’s Electronic Information Exchange System (EIES) dataset. The authors apply their measure to the commonly used network dataset, Freeman’s EIES dataset, which contains three different network relations used by researchers working on SNA.