EIGENVECTOR-BASED CENTRALITY MEASURES FOR TEMPORAL NETWORKS.

ABSTRACT: Numerous centrality measures have been developed to quantify the importances of nodes in time-independent networks, and many of them can be expressed as the leading eigenvector of some matrix. With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a principled generalization of network centrality measures that is valid for any eigenvector-based centrality. We consider a temporal network with N nodes as a sequence of T layers that describe the network during different time windows, and we couple centrality matrices for the layers into a supra-centrality matrix of size NT × NT whose dominant eigenvector gives the centr

SUBMITTER: Taylor D 

PROVIDER: S-EPMC5643020 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature