Anew centrality measure in dense networks based on two-way random walk betweenness

Highlights：

• We propose a new centrality measure based on random walk betweenness.

• We study and analyse the new metric as a betweenness centrality measure with common characteristics with Pagerank, presenting through its practical implementation in some examples based on synthetic, and testing with well-known real-world networks.

• Finally, in our experiments we show as our method (2RW) ranks similar to Pagerank, presenting common characteristics, although the idea behind the proposed centrality is closer to the CBT measure. In detail, the proposed metric increases the ranking of nodes belonging to dense clusters with a higher average degree than the remaining clusters, observing that it works better in dense networks. Moreover, we can detect the weakness of a network comparing CBT method with 2RW.