Temporal efficiency evaluation and small-worldness characterization in temporal networks.
Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks.
Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.
Zhongxiang
Z
Dai
School of Computer Engineering, Nanyang Technological University, Singapore.
Yu
Y
Chen
Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.
Junhua
J
Li
Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Johnson
J
Fam
Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.
Anastasios
A
Bezerianos
Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.
Yu
Y
Sun
Science. 2010 May 14;328(5980):876-8
20466926
Neuroimage. 2015 Jul 1;114:466-70
25818688
Phys Rev Lett. 2013 Apr 19;110(16):168701
23679648
PLoS Comput Biol. 2013;9(9):e1003171
24086116
Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Apr;81(4 Pt 2):046104
20481783
Science. 2002 May 3;296(5569):910-3
11988575
Neuroimage Clin. 2014 Jul 24;5:298-308
25161896
J Theor Biol. 2011 Feb 21;271(1):166-80
21130777
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Jul;84(1 Pt 2):016105
21867255
Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Mar;79(3 Pt 2):036115
19392026
Neuroimage. 2013 Oct 15;80:360-78
23707587
Nat Rev Neurosci. 2009 Mar;10(3):186-98
19190637
Neuroscientist. 2006 Dec;12(6):512-23
17079517
Neuroimage. 2010 Sep;52(3):1059-69
19819337
Proc Natl Acad Sci U S A. 2002 Jun 11;99(12):7821-6
12060727
Ann N Y Acad Sci. 2014 Jan;1307:89-103
24571182
Nat Neurosci. 2015 May;18(5):744-51
25849989
Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Apr;71(4 Pt 2):046119
15903738
Biomed Opt Express. 2015 Jun 05;6(7):2337-52
26203365
PLoS One. 2010 Jul 15;5(7):e11596
20657651
Curr Opin Neurol. 2010 Aug;23(4):341-50
20581686
Brain Struct Funct. 2015 Jan;220(1):101-15
24072164
Phys Rev Lett. 2001 Nov 5;87(19):198701
11690461
Proc Natl Acad Sci U S A. 2011 May 3;108(18):7641-6
21502525
Neuroinformatics. 2004;2(2):145-62
15319512
PLoS Comput Biol. 2007 Feb 2;3(2):e17
17274684
Nature. 1998 Jun 4;393(6684):440-2
9623998
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Oct;84(4 Pt 2):046118
22181238
Phys Rev E Stat Nonlin Soft Matter Phys. 2011 Oct;84(4 Pt 2):046117
22181237