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Weeks, Margaret R.; Clair, Scott; Borgatti, Stephen P.; Radda, Kim; Schensul, Jean J.

doi:10.1023/a:1015457400897

Cited by 106 publications

(16 citation statements)

References 33 publications

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“…It means the person is much more influenced by high-risk behavior of injection. This study’s results are proof to the claim that this study is clear and obvious and to the claim of being a meaningful study, we can mention Week’s and colleagues’ study in Hartford, America, 2001 that was in conformity with our study (19). As none of the demographic variables of this study were statistically significant, and based on the agreement of our study with other studies done in a network level like Valente and colleagues in America, 2000 (18), Lovell in France, 2002 (2), Shaw and colleagues in Canada, 2004 (20), Wylie and colleagues in Canada, 2006 (22), Unger and colleagues in America, 2006 (23), Havens and colleagues in America, 2011 (24), Tracy and colleagues in America, 2012 (25), and Shahesmaeili in Iran, 2014 (5), we can get that maybe because of data structure in the level of network, demographic variables like age and gender, have a slight influence on high-risk behavior of injection.…”

Section: Discussionsupporting

confidence: 88%

Relationship between a network's indicators and basic factors with high-risk behavior of injection among injecting drug users (IDU) via the multiple membership multilevel model

et al. 2017

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BackgroundAmong various methods and types of drug abuse, injection receives a great deal of importance because of its related dangerous behavior and health consequences. It seemed that some of the network’s indicators affect dangerous behavior of injection.ObjectiveTo determine the relationship between a network’s indicators and basic factors with high-risk behavior of injection among injecting drug users (IDU) via the multiple memberships multilevel model.MethodsIn this cross-sectional study, the data related to 147 IDUs in Kerman province, who were interviewed from October 2013 through March 2014, were used, and these addicts were chosen for interview from specific resorts used for common injection. In this study, for analyzing data, multiple membership multilevel model and MLwiN 2.02 software were used.ResultsIn this study, the mean age of people, who were mostly men, was 37.2±9.02. Based on the result, it becomes obvious that variables of in-degree with OR=1.49 (p=0.006) and the whole number of people related to the person with OR=1.18 (p=0.003) influences the high-risk behavior of injection. Also, none of the demographic variables influenced the high-risk behavior of injection.ConclusionTotally based on the results of this study, one can find a suitable method in the social network of IDUs in order to create essential strategies, reducing the risk throughout the country. In addition, in minimum time with fewer expenses, aggravation of dangerous behavior especially high-risk behavior of injection can be prevented.

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Section: Discussionsupporting

confidence: 88%

Relationship between a network's indicators and basic factors with high-risk behavior of injection among injecting drug users (IDU) via the multiple membership multilevel model

et al. 2017

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“…6.2 illustrates the links between the three primary ethnic groups in a drug users network. In some cases (namely for HBV and HIV), the analysis of network connections has led to develop prevention or control actions by detecting high-risk sites [569][570][571][572][573][574].…”

Section: Structurementioning

confidence: 99%

“…The nodes are clustered into the three primary ethnic groups considered in the analysis of Ref. [570]. is possible.…”

Section: Dynamics I: Opinion Formationmentioning

confidence: 99%

Complex networks: Structure and dynamics

et al. 2006

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Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is to model them as graphs whose nodes represent the dynamical units, and whose links stand for the interactions between them. On the one hand, scientists have to cope with structural issues, such as characterizing the topology of a complex wiring architecture, revealing the unifying principles that are at the basis of real networks, and developing models to mimic the growth of a network and reproduce its structural properties. On the other hand, many relevant questions arise when studying complex networks' dynamics, such as learning how a large ensemble of dynamical systems that interact through a complex wiring topology can behave collectively. We review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.

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“…When x * is a non-degenerate configuration (i.e, x * = x 0 , x N ), this indicates that sets of agents in the network are more vulnerable than others to the epidemics. We illustrate this by solving for the most-probable configuration using [20] under different λ µ , γ parameters for 2 realistic networks: a social network [8] and the Western United States power grid [9], obtained from [21] The network shown in Fig. 1 is a 193 node, 273 edge social network of drug users in Hartford, CT.…”

Section: Social Network and The Power Gridmentioning

confidence: 99%

Role of subgraphs in epidemics over finite-size networks under the scaled SIS process

Zhang¹,

Moura

2015

jcomplexnetw

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In previous work, we developed the scaled SIS process, which models the dynamics of SIS epidemics over networks. With the scaled SIS process, we can consider networks that are finite-sized and of arbitrary topology (i.e., we are not restricted to specific classes of networks). We derived for the scaled SIS process a closed-form expression for the time-asymptotic probability distribution of the states of all the agents in the network. This closed-form solution of the equilibrium distribution explicitly exhibits the underlying network topology through its adjacency matrix. This paper determines which network configuration is the most probable. We prove that, for a range of epidemics parameters, this combinatorial problem leads to a submodular optimization problem, which is exactly solvable in polynomial time. We relate the most-probable configuration to the network structure, in particular, to the existence of high density subgraphs. Depending on the epidemics parameters, subset of agents may be more likely to be infected than others; these more-vulnerable agents form subgraphs that are denser than the overall network. We illustrate our results with a 193 node social network and the 4941 node Western US power grid under different epidemics parameters.

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Untitled

Cited by 106 publications

References 33 publications

Relationship between a network's indicators and basic factors with high-risk behavior of injection among injecting drug users (IDU) via the multiple membership multilevel model

Relationship between a network's indicators and basic factors with high-risk behavior of injection among injecting drug users (IDU) via the multiple membership multilevel model

Complex networks: Structure and dynamics

Role of subgraphs in epidemics over finite-size networks under the scaled SIS process

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