32,814 research outputs found
A New Artificial Intelligence-Based Hierarchical K-Means Clustering Technique to Detect Addictive Twitter Activity
To stop the COVID-19 epidemic from spreading among their populations, several countries have implemented lockdowns. Students are being forced to stay at home during these lockdowns, which is causing them to use mobile phones, social media, and other digital technologies more frequently than ever. Their poor utilization of these digital tools may be detrimental to their emotional and mental health. In this study, we implement an Artificial Intelligence (AI) approach named Hierarchy-based K-Means Clustering (HKMC) algorithm to group individuals with comparable Twitter consumption habits to detect addictive Twitter activity during the epidemic. The effectiveness of the suggested HKMC is evaluated in terms of accuracy, precision, recall, and f1-score in respect to the association between students’ mental health and mobile phone dependency. Additionally, this study offers a comparative examination of both the suggested and existing procedures
Properties of highly clustered networks
We propose and solve exactly a model of a network that has both a tunable
degree distribution and a tunable clustering coefficient. Among other things,
our results indicate that increased clustering leads to a decrease in the size
of the giant component of the network. We also study SIR-type epidemic
processes within the model and find that clustering decreases the size of
epidemics, but also decreases the epidemic threshold, making it easier for
diseases to spread. In addition, clustering causes epidemics to saturate
sooner, meaning that they infect a near-maximal fraction of the network for
quite low transmission rates.Comment: 7 pages, 2 figures, 1 tabl
Impact of constrained rewiring on network structure and node dynamics
In this paper, we study an adaptive spatial network. We consider a susceptible-infected-susceptible (SIS) epidemic on the network, with a link or contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic.We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labeling and no epidemic dynamics, we provide both analytic and semianalytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network’s initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence
Contagions in Random Networks with Overlapping Communities
We consider a threshold epidemic model on a clustered random graph with
overlapping communities. In other words, our epidemic model is such that an
individual becomes infected as soon as the proportion of her infected neighbors
exceeds the threshold q of the epidemic. In our random graph model, each
individual can belong to several communities. The distributions for the
community sizes and the number of communities an individual belongs to are
arbitrary.
We consider the case where the epidemic starts from a single individual, and
we prove a phase transition (when the parameter q of the model varies) for the
appearance of a cascade, i.e. when the epidemic can be propagated to an
infinite part of the population. More precisely, we show that our epidemic is
entirely described by a multi-type (and alternating) branching process, and
then we apply Sevastyanov's theorem about the phase transition of multi-type
Galton-Watson branching processes. In addition, we compute the entries of the
matrix whose largest eigenvalue gives the phase transition.Comment: Minor modifications for the second version: added comments (end of
Section 3.2, beginning of Section 5.3); moved remark (end of Section 3.1,
beginning of Section 4.1); corrected typos; changed titl
Epidemic processes in complex networks
In recent years the research community has accumulated overwhelming evidence
for the emergence of complex and heterogeneous connectivity patterns in a wide
range of biological and sociotechnical systems. The complex properties of
real-world networks have a profound impact on the behavior of equilibrium and
nonequilibrium phenomena occurring in various systems, and the study of
epidemic spreading is central to our understanding of the unfolding of
dynamical processes in complex networks. The theoretical analysis of epidemic
spreading in heterogeneous networks requires the development of novel
analytical frameworks, and it has produced results of conceptual and practical
relevance. A coherent and comprehensive review of the vast research activity
concerning epidemic processes is presented, detailing the successful
theoretical approaches as well as making their limits and assumptions clear.
Physicists, mathematicians, epidemiologists, computer, and social scientists
share a common interest in studying epidemic spreading and rely on similar
models for the description of the diffusion of pathogens, knowledge, and
innovation. For this reason, while focusing on the main results and the
paradigmatic models in infectious disease modeling, the major results
concerning generalized social contagion processes are also presented. Finally,
the research activity at the forefront in the study of epidemic spreading in
coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio
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