11 research outputs found
Centrality Measures for Networks with Community Structure
Understanding the network structure, and finding out the influential nodes is
a challenging issue in the large networks. Identifying the most influential
nodes in the network can be useful in many applications like immunization of
nodes in case of epidemic spreading, during intentional attacks on complex
networks. A lot of research is done to devise centrality measures which could
efficiently identify the most influential nodes in the network. There are two
major approaches to the problem: On one hand, deterministic strategies that
exploit knowledge about the overall network topology in order to find the
influential nodes, while on the other end, random strategies are completely
agnostic about the network structure. Centrality measures that can deal with a
limited knowledge of the network structure are required. Indeed, in practice,
information about the global structure of the overall network is rarely
available or hard to acquire. Even if available, the structure of the network
might be too large that it is too much computationally expensive to calculate
global centrality measures. To that end, a centrality measure is proposed that
requires information only at the community level to identify the influential
nodes in the network. Indeed, most of the real-world networks exhibit a
community structure that can be exploited efficiently to discover the
influential nodes. We performed a comparative evaluation of prominent global
deterministic strategies together with stochastic strategies with an available
and the proposed deterministic community-based strategy. Effectiveness of the
proposed method is evaluated by performing experiments on synthetic and
real-world networks with community structure in the case of immunization of
nodes for epidemic control.Comment: 30 pages, 4 figures. Accepted for publication in Physica A. arXiv
admin note: text overlap with arXiv:1411.627
Profile Closeness in Complex Networks
We introduce a new centrality measure, known as profile closeness, for
complex networks. This network attribute originates from the graph-theoretic
analysis of consensus problems. We also demonstrate its relevance in inferring
the evolution of network communities.
Keywords: Complex networks, Centrality, Community, Median, Closeness,
Consensus theor
Overcoming vaccine hesitancy by multiplex social network targeting: An analysis of targeting algorithms and implications
Incorporating social factors into disease prevention and control efforts is
an important undertaking of behavioral epidemiology. The interplay between
disease transmission and human health behaviors, such as vaccine uptake,
results in complex dynamics of biological and social contagions. Maximizing
intervention adoptions via network-based targeting algorithms by harnessing the
power of social contagion for behavior and attitude changes largely remains a
challenge. Here we address this issue by considering a multiplex network
setting. Individuals are situated on two layers of networks: the disease
transmission network layer and the peer influence network layer. The disease
spreads through direct close contacts while vaccine views and uptake behaviors
spread interpersonally within a potentially virtual network. The results of our
comprehensive simulations show that network-based targeting with pro-vaccine
supporters as initial seeds significantly influences vaccine adoption rates and
reduces the extent of an epidemic outbreak. Network targeting interventions are
much more effective by selecting individuals with a central position in the
opinion network as compared to those grouped in a community or connected
professionally. Our findings provide insight into network-based interventions
to increase vaccine confidence and demand during an ongoing epidemic.Comment: 16 pages, 8 figures. Comments are welcom
Social Resilience of Street Vendors at Gading Serpong Real Estate Area, Tangerang, Indonesia
Using correlation-based hierarchical analysis and synchronization analysis, this study focuses on monthly price indices for residential homes, office buildings, and retail properties in ten major Chinese cities for the years 2005 to 2021. Through these analyses, one can identify interactions and interdependence among the price indices, heterogeneous patterns in synchronizations of the price indices, and their evolving paths with time. Empirical findings suggest that the degree of real estate price comovements across all property types and cities is relatively low and stable from January 2017 to February 2020, followed by significant increases during the COVID-19 pandemic from March 2020 to January 2021 and significant decreases since February 2021 with the recovery of the economy. Several groups of property types and cities are determined in this study, each of which having its members reveal rather strong but volatile synchronizations of price indices. Rolling importance analysis does not suggest persistent increasing or decreasing trends for the real estate price associated with a specific property type and city. Policy studies on real estate price comovements may benefit from these findings here
Topology Reconstruction of Dynamical Networks via Constrained Lyapunov Equations
The network structure (or topology) of a dynamical network is often
unavailable or uncertain. Hence, we consider the problem of network
reconstruction. Network reconstruction aims at inferring the topology of a
dynamical network using measurements obtained from the network. In this
technical note we define the notion of solvability of the network
reconstruction problem. Subsequently, we provide necessary and sufficient
conditions under which the network reconstruction problem is solvable. Finally,
using constrained Lyapunov equations, we establish novel network reconstruction
algorithms, applicable to general dynamical networks. We also provide
specialized algorithms for specific network dynamics, such as the well-known
consensus and adjacency dynamics.Comment: 8 page