Skip navigation

Random walk centrality for temporal networks

Random walk centrality for temporal networks

Rocha, Luis E. C. ORCID: 0000-0001-9046-8739 and Masuda, Naoki (2014) Random walk centrality for temporal networks. New Journal of Physics, 16 (6):063023. ISSN 1367-2630 (Online) (doi:https://doi.org/10.1088/1367-2630/16/6/063023)

[img]
Preview
PDF (Publisher's PDF - Open Access)
19637 ROCHA_Random_Walk_Centrality_for_Temporal_Networks_2014.pdf - Published Version
Available under License Creative Commons Attribution.

Download (3MB) | Preview

Abstract

Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included.

Item Type: Article
Additional Information: Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.
Uncontrolled Keywords: Random Walks, Page-Rank, Temporal Networks
Subjects: Q Science > QC Physics
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Faculty of Business > Department of International Business & Economics
Last Modified: 08 May 2019 23:21
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: GREAT b
Selected for GREAT 2019: GREAT 3
URI: http://gala.gre.ac.uk/id/eprint/19637

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics