11 research outputs found
Layer-switching cost and optimality in information spreading on multiplex networks
We study a model of information spreading on multiplex networks, in which
agents interact through multiple interaction channels (layers), say online vs.\
offline communication layers, subject to layer-switching cost for transmissions
across different interaction layers. The model is characterized by the
layer-wise path-dependent transmissibility over a contact, that is dynamically
determined dependently on both incoming and outgoing transmission layers. We
formulate an analytical framework to deal with such path-dependent
transmissibility and demonstrate the nontrivial interplay between the
multiplexity and spreading dynamics, including optimality. It is shown that the
epidemic threshold and prevalence respond to the layer-switching cost
non-monotonically and that the optimal conditions can change in abrupt
non-analytic ways, depending also on the densities of network layers and the
type of seed infections. Our results elucidate the essential role of
multiplexity that its explicit consideration should be crucial for realistic
modeling and prediction of spreading phenomena on multiplex social networks in
an era of ever-diversifying social interaction layers.Comment: 15 pages, 7 figure
Epidemic Spreading with Heterogeneous Awareness on Human Networks
The spontaneous awareness behavioral responses of individuals have a significant impact on epidemic spreading. In this paper, a modified Susceptible-Alert-Infected-Susceptible (SAIS) epidemic model with heterogeneous awareness is presented to study epidemic spreading in human networks and the impact of heterogeneous awareness on epidemic dynamics. In this model, when susceptible individuals receive awareness information about the presence of epidemic from their infected neighbor nodes, they will become alert individuals with heterogeneous awareness rate. Theoretical analysis and numerical simulations show that heterogeneous awareness can enhance the epidemic threshold with certain conditions and reduce the scale of virus outbreaks compared with no awareness. What is more, for the same awareness parameter, it also shows that heterogeneous awareness can slow effectively the spreading size and does not delay the arrival time of epidemic spreading peak compared with homogeneous awareness
Cascading Failures in Complex Networks
Cascading failure is a potentially devastating process that spreads on
real-world complex networks and can impact the integrity of wide-ranging
infrastructures, natural systems, and societal cohesiveness. One of the
essential features that create complex network vulnerability to failure
propagation is the dependency among their components, exposing entire systems
to significant risks from destabilizing hazards such as human attacks, natural
disasters or internal breakdowns. Developing realistic models for cascading
failures as well as strategies to halt and mitigate the failure propagation can
point to new approaches to restoring and strengthening real-world networks. In
this review, we summarize recent progress on models developed based on physics
and complex network science to understand the mechanisms, dynamics and overall
impact of cascading failures. We present models for cascading failures in
single networks and interdependent networks and explain how different dynamic
propagation mechanisms can lead to an abrupt collapse and a rich dynamic
behavior. Finally, we close the review with novel emerging strategies for
containing cascades of failures and discuss open questions that remain to be
addressed.Comment: This review has been accepted for publication in the Journal of
Complex Networks Published by Oxford University Pres
Discordant attributes of structural and functional brain connectivity in a two-layer multiplex network.
Several studies have suggested that functional connectivity (FC) is constrained by the underlying structural connectivity (SC) and mutually correlated. However, not many studies have focused on differences in the network organization of SC and FC, and on how these differences may inform us about their mutual interaction. To explore this issue, we adopt a multi-layer framework, with SC and FC, constructed using Magnetic Resonance Imaging (MRI) data from the Human Connectome Project, forming a two-layer multiplex network. In particular, we examine node strength assortativity within and between the SC and FC layer. We find that, in general, SC is organized assortatively, indicating brain regions are on average connected to other brain regions with similar node strengths. On the other hand, FC shows disassortative mixing. This discrepancy is apparent also among individual resting-state networks within SC and FC. In addition, these patterns show lateralization, with disassortative mixing within FC subnetworks mainly driven from the left hemisphere. We discuss our findings in the context of robustness to structural failure, and we suggest that discordant and lateralized patterns of associativity in SC and FC may provide clues to understand laterality of some neurological dysfunctions and recovery
Percolation and reinforcement on complex networks
Complex networks appear in almost every aspect of our daily life and are widely studied in
the fields of physics, mathematics, finance, biology and computer science. This work utilizes
percolation theory in statistical physics to explore the percolation properties of
complex networks and develops a reinforcement scheme on improving network resilience.
This dissertation covers two major parts of my Ph.D. research on complex networks:
i) probe—in the context of both traditional percolation and k-core percolation—the resilience
of complex networks with tunable degree distributions or directed dependency links under
random, localized or targeted attacks; ii) develop and propose a
reinforcement scheme to eradicate catastrophic collapses that occur very often in interdependent networks.
We first use generating function and probabilistic methods to obtain analytical solutions to
percolation properties of interest, such as the giant component size and the critical occupation probability.
We study uncorrelated random networks with Poisson, bi-Poisson, power-law, and Kronecker-delta degree
distributions and construct those networks which are based on the configuration model.
The computer simulation results show remarkable agreement
with theoretical predictions.
We discover an increase of network robustness as the degree distribution
broadens and a decrease of network robustness as directed dependency links come into play
under random attacks. We also find that targeted attacks exert the biggest damage to
the structure of both single and interdependent networks in k-core percolation.
To strengthen the resilience of interdependent networks, we develop and propose a reinforcement
strategy and obtain the critical amount of reinforced nodes analytically for interdependent
Erdős-Rényi networks and numerically for scale-free and for random regular networks.
Our mechanism leads to improvement of network stability of the West U.S. power grid.
This dissertation provides us with a deeper understanding of the effects of structural features on network
stability and fresher insights into designing resilient interdependent infrastructure networks