TY - JOUR
T1 - A new betweenness centrality measure based on an algorithm for ranking the nodes of a network
JO - Applied Mathematics and Computation
VL - 244
IS - 0
SP - 467
EP - 478
PY - 2014/10/1/
T2 -
AU - Agryzkov, Taras
AU - Oliver, Jose L.
AU - Tortosa, Leandro
AU - Vicent, Jose
SN - 0096-3003
DO - http://dx.doi.org/10.1016/j.amc.2014.07.026
UR - http://www.sciencedirect.com/science/article/pii/S0096300314009837
KW - Street network algorithms
KW - PageRank algorithms
KW - Centrality measures
KW - Betweenness
KW - Random-walk betweenness
KW - Eigenvector centrality
AB - Abstract
We propose and discuss a new centrality index for urban street patterns represented as networks in geographical space. This centrality measure, that we call ranking-betweenness centrality, combines the idea behind the random-walk betweenness centrality measure and the idea of ranking the nodes of a network produced by an adapted PageRank algorithm. We initially use a PageRank algorithm in which we are able to transform some information of the network that we want to analyze into numerical values. Numerical values summarizing the information are associated to each of the nodes by means of a data matrix. After running the adapted PageRank algorithm, a ranking of the nodes is obtained, according to their importance in the network. This classification is the starting point for applying an algorithm based on the random-walk betweenness centrality. A detailed example of a real urban street network is discussed in order to understand the process to evaluate the ranking-betweenness centrality proposed, performing some comparisons with other classical centrality measures.
ER -