Negative and Positive Effects of Clustering Coefficient
A semi-local and free-parameter centrality measure by applying the natural characteristics of complex networks. The proposed centrality can assign higher ranks for structural holes as better spreaders in the network. It uses the positive effects of second-level neighbors’ clustering coefficient and negative effects of node's clustering coefficient in defining the importance of nodes. Therefore, the proposed centrality avoids selection of spreaders that are too close to one another.
- Berahmand, K., Bouyer, A. and Samadi, N., 2018. A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks. Chaos, Solitons & Fractals, 110, pp.41-54. DOI: 10.1016/j.chaos.2018.03.014
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