Homophily and heterophily in personal networks. From mutual acquaintance to relationship intensity

Lozares, Carlos; Verd, Joan Miquel; Cruz, Irene; Barranco, Oriol

doi:10.1007/s11135-013-9915-4

Cited by 34 publications

(21 citation statements)

References 27 publications

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“…However, there are systems which behave in opposite manner, where their components preferentially choose to connect themselves to others with some distinct inner characteristics [3]. In fact, Nature seems to favor the former or the later construction, which we respectively call Similar or Dissimilar (neighborhood) patterns, to achieve different agendas [4]. This article explores ideas inspired by these scenarios within the non-identical phase oscillator Kuramoto model with local mean field coupling, which is one of the main paradigms to describe collective behavior and synchronization [5].…”

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confidence: 99%

Synchronization versus neighborhood similarity in complex networks of nonidentical oscillators

2015

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Does the assignment order of a fixed collection of slightly distinct subsystems into given communication channels influence the overall ensemble behavior? We discuss this question in the context of complex networks of nonidentical interacting oscillators. Three types of connection configurations are considered: Similar, Dissimilar, and Neutral patterns. These different groups correspond, respectively, to oscillators alike, distinct, and indifferent relative to their neighbors. To construct such scenarios we define a vertex-weighted graph measure, the total dissonance, which comprises the sum of the dissonances between all neighbor oscillators in the network. Our numerical simulations show that the more homogeneous a network, the higher tend to be both the coupling strength required for phase locking and the associated final phase configuration spread over the circle. On the other hand, the initial spread of partial synchronization occurs faster for Similar patterns in comparison to Dissimilar ones, while neutral patterns are an intermediate situation between both extremes.

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confidence: 99%

Synchronization versus neighborhood similarity in complex networks of nonidentical oscillators

2015

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“…Taking into account that the data did not meet the criteria of normality and homoscedasticity, we proceeded with the non-parametric Kruskal-Wallis analysis to identify whether there are significant differences in the three clusters detected as a function of the level of empowerment, using the values of the full ordinal scale for this analysis. This analysis did not show significant values, even with the time of relocation in the target community, but it did show significant values as a function of the age of the alters ( χ 2 = 56,214; p = 0.000), a situation that can be considered normal considering the principle of homophily ( McPherson et al, 2001 ), according to which people tend to establish substantive relationships with people of a similar age ( Lozares et al, 2014 ).…”

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confidence: 82%

Community Participation and Empowerment in a Post-disaster Environment: Differences Tied to Age and Personal Networks of Social Support

et al. 2020

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In this article, an attempt was made to identify the level of community social participation according to age, gender, and the structural characteristics of the personal support networks in a population displaced by floods in the Colombian Caribbean. The research was based on a non-experimental methodology with an associative-relational strategy. An intentional non-probabilistic sample of 151 people affected by the winter wave in the south of the Department of Atlántico (Colombia) was selected. In total, the study included 42 males (27.8%) and 109 females (72.2%) participants, with an average age of 37.48 (±14, ranging from 18 to 80) and average relocation time of 21.79 months (±8.22, ranging from 5 to 36). The Arizona Social Support Interview Schedule ( ASSIS ) and Community Empowerment instruments were responded to with an instrument adapted from the leadership competence factor. The results show lower rates of intermediation in the older population, and the relationship between social participation and gender shows equally cohesive social support networks in men and women. This evidence is discussed to promote psychosocial interventions aimed to increase community engagement and empowerment of people that have experienced non-voluntary mobility processes.

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“…The opposite, where individuals relate to those with different characteristics is called heterophily and is seen in some social networks; for example, see Lozares et al (2014).…”

Section: Discussion and Future Workmentioning

confidence: 99%

Hierarchical models for independence structures of networks

Sadeghi

Rinaldo

2019

Statistica Neerlandica

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We introduce a new family of network models, called hierarchical network models, that allow to represent in an explicit manner the stochastic dependence among the edges. In particular, each member of this family can be associated with a graphical model defining conditional independence clauses among the edges of the network, called the dependency graph. Every network model of dyadic independence assumption can be generalized to construct members of this new family. Using this new framework, we generalize the Erdös-Rényi and β-models to create hierarchical Erdös-Rényi and β-models. We describe various methods for parameter estimation as well as simulation studies for models with sparse dependency graphs. We also provide a comprehensive discussion on open problems related to these newly defined models. 1 arXiv:1605.04565v1 [stat.ME] 15 May 2016There is a natural duality between networks and dependency graphs: edges of the network are (binary) random variables, and hence can be considered to be nodes of a dependency graph. Edges of the dependency graph would then determine the conditional independence among these variables, i.e. the independence structure among the edges of the network.The first, and possibly the only, place where this duality has been used is in Frank and Strauss (1986), where they assumed the dependency graph to be the line graph of the complete graph, as defined in the graph theory literature. However, there have recently been other approaches to deal with certain "local" types of dependency in networks by using nodal attributes, i.e. extra information on the individuals presented by nodes of the network; see, for example, Schweinberger and Handcock (2012) and Fellows and Handcock (2012).The main goal of this paper is to exploit this duality between networks and dependency graphs. We basically use the model associated with a given dependency graph, and combine it with known network models that assume dyadic independence by further parametrizing the graphical model using the parameters in the network model. This allows us to derive new network models that preserve the independence structure of the given dependency graph and has similar properties to those of the original network model. For this purpose, we work with undirected graphical models, which imply that our proposed network models are of linear exponential family form. 3In this paper, we demonstrate our approach for three well-known classes of network models: The Erdös-Rényi models, defined by Erdös and Rényi (1959) and studied vastly in the literature of networks and random graph theory, the β-models, defined by Blitzstein and Diaconis (2010); Chatterjee et al. (2011) and recently studied by Rinaldo et al. (2013) for undirected networks, and the p 1 models defined by Holland and Leinhardt (1981) for directed networks.We also provide a method based on the gradient decent algorithm to estimate the maximum of the likelihood function for hierarchical Erdös-Rényi models. In principle, this method can be generalized to other families of hierarc...

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Homophily and heterophily in personal networks. From mutual acquaintance to relationship intensity

Cited by 34 publications

References 27 publications

Synchronization versus neighborhood similarity in complex networks of nonidentical oscillators

Synchronization versus neighborhood similarity in complex networks of nonidentical oscillators

Community Participation and Empowerment in a Post-disaster Environment: Differences Tied to Age and Personal Networks of Social Support

Hierarchical models for independence structures of networks

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