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In their research paper titled, "Birds of a Feather: We test for assortative mixing by using t tests of mean differences, Kolmogorov—Smirnov tests of distributional differences, logistic regression and hazard rate models of the rate of Go adoption.

IM users who adopted Yahoo! Application of this framework to a network of 27 million individuals connected by instant message traffic provides an estimate of the degree to which peer influence and homophily affect the diffusion of a new mobile service application across this network.

A large number of excess links caused by degree heterogeneity might be confused with homophily. When a Facebook user likes or interacts with an article or post of a certain ideology, Facebook, continues to show that user posts of that similar ideology that Facebook believes they will be drawn to.

In our context and in many important contexts link formation is not likely to be driven by the behavior in question—Go adoption is unlikely to drive friendship. Although not as salient, the link density of the network might also lead to short-term, localized deviations in the shape of the distribution.

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The social homogeneity of most organizations creates a strong baseline homophily in networks that are formed there. Although the matching process accounts for homophily on all observed characteristics and those unobserved or latent characteristics that are correlated with what we observe, unobserved and uncorrelated latent homophily and unobserved confounding factors or contextual effects such as correlated exposure to advertising among friends or information from common unobserved friends may also contribute to assortative mixing and temporal clustering.

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Family relationships often produce relatively close, frequent contact among those who are at great geographic distance. Long-run integration states that the types of neighbouring nodes of any node will converge to the global distribution of types of the network as a whole which eliminates biases among neighbouring nodes.

Convergence is explained by three models of integration: These findings, and the general statistical methods used to identify them, document the conditions under which peer influence exists and can help verify the implications of a broad class of social contagion models in a variety of contexts and disciplines 40 We estimate homophily over time by constructing a vector of 20 personal, behavioral, and local network attributes Table S7 and measure the cosine distance defined for vectors of characteristics, xi and xj for nodes i and j as follows: Instant Messenger IM network where each node is an IM user for whom we collected detailed demographic, geographic, and behavioral information as well as daily IM message traffic.

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Understanding the dynamic mechanisms that govern contagion processes in networks is critical in numerous scientific disciplines and for the development of effective social policy, public health actions, and marketing strategies. Second, a distinct but related body of literature examines selection and influence processes in the co-evolution of behaviors and network structure in cases where tie formation is likely to be a function of the behavior in question [see Snijders et al.

Homophily in Social Networks," McPherson, Smith-Lovin, and Cook write that homogenous personal networks result in limited "social worlds in a way that has powerful implications for the information they receive, the attitudes the form, and the interactions they experience.

This can be explained by the argument that high levels of homophily may significantly decrease the viability of networks, hence making convexity less frequent in complex networks.

Consequently, there is a level of homophily such that the two effects cancel each other out and the cumulative degree distribution reaches a linear shape in a log-log scale.

We suspect that peer influence effects differ for products with direct network externalities and therefore encourage the application of these methods to influence estimation in the adoption of such products. People who have demographic similarity tend to own shared knowledge, and therefore they have a greater ease of communication and share cultural tastes, which can also generate homophily.

Because similar articles are likely to cite similar references bias may arise in the formation of connections. Large clusters of adopters tend to be more similar to one another, creating greater risk of overestimation of influence in the very cliques that seem to be the most susceptible to contagious spread.

Partial integration causes the distribution of type in neighbouring nodes to converge monotonically to the global distribution with time albeit with some bias in the limit. A and B All treated adopters filled circles and the number of treated adopters that can be explained by homophily open circles per day A and cumulatively over time B.

Reviews of Modern Physics We find that regression results are qualitatively similar in both cases, but that model fit is significantly better when the logarithm is used.

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Men and women frequently live together, and are both large and equally-sized populations. Race and ethnicity also correlates with educational attainment and occupation, which increase baseline homophily further.

Go between June 1,and October 29, Our work is not without limitations. In the development of the shape of the cumulative degree distribution curve the effects of the link structure of existing nodes among themselves and with new nodes and homophily work against each other, with the former leading to concavity and homophily causing convexity.

Smaller groups have lower diversity simply due to the number of members, and this tends to give racial and ethnic minority groups a higher baseline homophily.

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The connections between people who occupy equivalent roles will induce homophily in the system of network ties. To assess the aggregate effect of peer influence on Go adoption across the entire population, we compute the fraction of treated to untreated adopters and use it to estimate the gross number of adopters who would have adopted had they not been treated had they not had an adopter friend.

Technology such as the telephone, e-mail, and social networks have reduced by not eliminated this effect.

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