Identification of Influential Node in a Complex Network using WSM (Weighted Sum Model)

Abstract

Identification of influential nodes is a significant issue in the structural analysis of complex network. To address this issue, different centrality measures have been proposed such as betweenness centrality, closeness centrality, degree centrality, but all of them suffered from some drawbacks. This research proposes a new method to identify influential nodes based on Weighted Sum Method (WSM). Weighted Sum Method is one of the widely used and simplest multi-criteria decision making method. In this paper, different centrality measures are considered as the multi-attribute of complex network, so as to take advantage of each centrality measure. The multi-attribute of complex network are aggregated using WSM to compute the importance of each node in the network. The efficacy and feasibility of the proposed method is established by conducting experiments on four real-world networks and an informative network.