2 research outputs found
Identifying influential nodes in complex networks: Effective distance gravity model
The identification of important nodes in complex networks is an area of
exciting growth due to its applications across various disciplines like disease
controlling, community finding, data mining, network system controlling, just
to name a few. Many measures have thus been proposed to date, and these
measures are either based on the locality of nodes or the global nature of the
network. These measures typically use distance based on the concept of
traditional Euclidean Distance, which only focus on the local static geographic
distance between nodes but ignore the interaction between nodes in real-world
networks. However, a variety of factors should be considered for the purpose of
identifying influential nodes, such as degree, edge, direction and weight. Some
methods based on evidence theory have also been proposed. In this paper, we
have proposed an original and novel gravity model with effective distance for
identifying influential nodes based on information fusion and multi-level
processing. Our method is able to comprehensively consider the global and local
information of the complex network, and also utilize the effective distance to
replace the Euclidean Distance. This allows us to fully consider the complex
topological structure of the real network, as well as the dynamic interaction
information between nodes. In order to validate the effectiveness of our
proposed method, we have utilized the susceptible infected (SI) model to carry
out a variety of simulations on eight different real-world networks using six
existing well-known methods. The experimental results indicate the
reasonableness and effectiveness of our proposed method
The network asymmetry caused by the degree correlation and its effect on the bimodality in control
Our ability to control a whole network can be achieved via a small set of
driver nodes. While the minimum number of driver nodes needed for control is
fixed in a given network, there are multiple choices for the driver node set. A
quantity used to investigate this multiplicity is the fraction of redundant
nodes in the network, referring to nodes that do not need any external control.
Previous work has discovered a bimodality feature characterized by a
bifurcation diagram: networks with the same statistical property would stay
with equal probability to have a large or small fraction of redundant nodes.
Here we find that this feature is rooted in the symmetry of the directed
network, where both the degree distribution and the degree correlation can play
a role. The in-in and out-out degree correlation will suppress the bifurcation,
as networks with such degree correlations are asymmetric under network
transpose. The out-in and in-out degree correlation do not change the network
symmetry, hence the bimodality feature is preserved. However, the out-in degree
correlation will change the critical average degree needed for the bifurcation.
Hence by fixing the average degree of networks and tuning out-in degree
correlation alone, we can observe a similar bifurcation diagram. We conduct
analytical analyses that adequately explain the emergence of bimodality caused
by out-in degree correlation. We also propose a quantity, taking both degree
distribution and degree correlation into consideration, to predict if a network
would be at the upper or lower branch of the bifurcation. As is well known that
most real networks are not neutral, our results extend our understandings of
the controllability of complex networks.Comment: 25 pages, 8 figures, to be published in Physica A: Statistical
Mechanics and its Application