Non-backtracking PageRank: From the classic model to hashimoto matrices

Non-backtracking centrality was introduced as a way to correct what may be understood as a deficiency in the eigenvector centrality, since the eigenvector centrality in a network can be artificially increased in high-degree nodes (hubs) because a hub is central because its neighbors are central, but these, in turn, are central just because they are hub neighbors. We define the non-backtracking PageRank as a new measure modifying the well-known classic PageRank in order to avoid the possibility of the random walker returning to the node immediately visited (non-backtracking walk). But, as we show, this measure presents a gap and a remarkable difference between the limit of “no penalty for return trips” and the direct calculation of the non-backtracking PageRank. Also, as it is shown in the applications presented, in certain cases this new measure produces notable variations with respect to the classifications obtained by the classic PageRank.