Transitivity and degree assortativity explained: The bipartite structure of social networks

Filho, Demival Vasques; O’Neale, Dion R. J.

doi:10.1103/physreve.101.052305

Cited by 25 publications

(19 citation statements)

References 13 publications

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“…Tracking this heterogeneity through multiple epidemic generations to account for clustering would be a complicated calculation, but one should that should lead to much better approximations of the epidemic thresholds. Finally, we focused on homogeneous but clustered contact networks to isolate the impact of clustering on parasitic contagions, but the role of correlations and of the internal structure of communities could also have nontrivial consequences [25,26]. Are nodes with higher membership part of larger or smaller communities than expected?…”

Section: Discussionmentioning

confidence: 99%

Spread of infectious disease and social awareness as parasitic contagions on clustered networks

Mistry

Althouse

2020

Phys. Rev. Research

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There is a rich history of models for the interaction of a biological contagion like influenza with the spread of related information such as an influenza vaccination campaign. Recent work on the spread of interacting contagions on networks has highlighted that these interacting contagions can have counterintuitive interplay with network structure. Here, we generalize one of these frameworks to tackle three important features of the spread of awareness and disease: one, we model the dynamics on highly clustered, cliquish, networks to mimic the role of workplaces and households; two, the awareness contagion affects the spread of the biological contagion by reducing its transmission rate where an aware or vaccinated individual is less likely to be infected; and three, the biological contagion also affects the spread of the awareness contagion but by increasing its transmission rate where an infected individual is more receptive and more likely to share information related to the disease. Under these conditions, we find that increasing network clustering, which is known to hinder disease spread, can actually allow them to sustain larger epidemics of the disease in models with awareness. This counterintuitive result goes against the conventional wisdom suggesting that random networks are justifiable as they provide worst-case scenario forecasts. To further investigate this result, we provide a closed-form criterion based on a two-step branching process (i.e., the numbers of expected tertiary infections) to identify different regions in parameter space where the net effect of clustering and coinfection varies. Altogether, our results highlight once again the need to go beyond random networks in disease modeling and illustrate the type of analysis that is possible even in complex models of interacting contagions.

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Section: Discussionmentioning

confidence: 99%

Spread of infectious disease and social awareness as parasitic contagions on clustered networks

Mistry

Althouse

2020

Phys. Rev. Research

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“…Bipartite projections appear in many contexts (Vasques Filho & O'Neale, 2020), including spatial analysis, where they can take two distinct forms depending on whether spatial entities (i.e., locations) are treated agents or artifacts. In the locations-as-agents approach, a spatial bipartite projection is a network of locations, such that a pair of locations is connected to the extent that they share artifacts.…”

Section: Bipartite Projection Network In Spatial Analysismentioning

confidence: 99%

Analysis of spatial networks from bipartite projections using the R backbone package

Neal¹,

Domagalski²,

Sagan³

2020

Preprint

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Spatial networks can be difficult to measure. Bipartite projections offer a potential solution by indirectly inferring a network from data that is easier to collect. They are now used in many subfields of geography, and are among the most common ways to measure the world city network, where intercity links are inferred from firm co-location patterns. However, spatial bipartite projections are difficult to analyze because the links in these networks are weighted, and larger weights do not necessarily indicate stronger or more important connections. Methods for extracting the backbone of bipartite projections offer a solution by using statistical models for identifying the links that have statistically significant weights. In this paper, we introduce the open-source backbone R package, which implements several backbone models, and demonstrate its key features by using it to measure a world city network.

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“…2)). When an individual becomes the second point of intersection of two open triplets (27). Larger circles result in a more clustered social structure; several people being part of more circles results in a less clustered social structure.…”

Section: Generalizing We Havementioning

confidence: 99%

“…I use three types of data, with two data sets of each type. The first type represents scientific collaborations on papers in biology and mathematics [27]. I build two-mode networks connecting authors to papers they have published; the one-mode projections are the coauthorship networks,…”

Section: S5mentioning

confidence: 99%

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Mechanisms and emergent properties of social structure: the duality of actors and social circles

Filho¹

2020

Preprint

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I propose a theory of social structure that challenges the widely accepted role of preferential attachment and triadic closure as primary mechanisms of network formation. For this, I build upon Feld's concept of social circles, Breiger’s concept of the duality of actors and groups, and Hinde’s concept of interactions and relationships. The theory emphasizes that ties between actors arise and evolve according to social circles and social situations in which they participate, a notion straightforwardly modeled through two-mode and projected networks. Using recent results aided by analyses of empirical and artificial networks, I argue that structural properties such as tie strength, heterogeneity of popularity and strength among actors, clustering, community formation, and segregation emerge from homophily, jointly with overlap and social activity—mechanisms introduced in this study. The mechanisms form the two-mode network, and these structural properties naturally arise in the one-mode projection. The results show that social circle and social situation size distributions modulate network structures by interweaving with social activity distributions, and that overlap increases segregation from a network viewpoint. This theory’s implications are broad, affecting several social processes ranging from social cohesion, tolerance, and child development to the spread of infectious diseases.

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Transitivity and degree assortativity explained: The bipartite structure of social networks

Cited by 25 publications

References 13 publications

Spread of infectious disease and social awareness as parasitic contagions on clustered networks

Spread of infectious disease and social awareness as parasitic contagions on clustered networks

Analysis of spatial networks from bipartite projections using the R backbone package

Mechanisms and emergent properties of social structure: the duality of actors and social circles

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