Combining of Existing Centrality Measures


To identify the influential nodes of different networks, different centrality measures should be taken into account according to the characteristics of the network. So a new method is proposed to identify influential nodes based on combining the existing centrality measures.The proposed method can aggregate multiple centrality measures from different aspects to make the assessment

$$r_i=median\left\{ r_u^i,r_l^i\right\}$$

where $r_i$ denotes the final ranking of each node, $r_u^i$ represents the maximum ranking of node $i$, and $r_l^i$ represents the minimum ranking of node $i$.


  • Fei L., Mo H., Deng Y., 2017. A new method to identify influential nodes based on combining of existing centrality measures. Modern Physics Letters B, 31(26). DOI: 10.1142/S0217984917502438 Publisher web site


There are pros and cons of this approach. This is discussed in

Keng, Y. Y., Kwa, K. H., & McClain, C., 2020. Convex combinations of centrality measures. The Journal of Mathematical Sociology. DOI: 10.1080/0022250X.2020.1765776.

Besides, this article proposes another approach to combine centrality measures.

Kwa Kiam Heong

Add Replay written June 25, 2021, 1:40 pm by Anonymous User

Add your comment

Sum of    and  

The rendering mode: