The power of any kind of network approach lies in the ability to simplify a complex system so
that one can better understand its function as a whole. Sometimes it is beneficial, however …

Temporal networks, ie, networks in which the interactions among a set of elementary units
change over time, can be modelled in terms of time-varying graphs, which are time-ordered …

Ebola is a deadly infectious virus that spreads very quickly through human-to-human
transmission and sometimes death. The continuous detection and remote monitoring of …

Complex network theory (CNT) has been providing the platform to simulate, analyze, and
visualize different complex interdependent networks. Despite the successes of simulating …

Certain described examples are directed towards analyzing network data. The network data
is processed to generate a graph data structure that has edges that are associated with …

We propose the Temporal Walk Centrality, which quantifies the importance of a node by
measuring its ability to obtain and distribute information in a temporal network. In contrast to …

The betweenness centrality of a vertex v is an important centrality measure that quantifies
how many optimal paths between pairs of other vertices visit v. Computing betweenness …

Dynamic networks can be characterised by many factors such as changes (eg, vulnerability
change, update of applications and services, topology changes). It is of vital importance to …

Small populations (eg, hospitals, schools or workplaces) are characterised by high contact
heterogeneity and stochasticity affecting pathogen transmission dynamics. Empirical …

The analysis of the topology and organization of brain networks is known to greatly benefit
from network measures in graph theory. However, to evaluate dynamic changes of brain …