Modeling Social Network Topologies in Elementary Schools

Huerta-Quintanilla, R.; Canto-Lugo, Efrain; Alva, Dolores Viga-de

doi:10.1371/journal.pone.0055371

Cited by 13 publications

(9 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We began by analyzing the survey results reported in which three elementary schools were considered [ 27 ]. The networks extracted from the three schools: E1, E2, and E3 are made up of 108, 226, and 419 students, respectively.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Friendship Concept and Community Network Structure among Elementary School and University Students

et al. 2016

Self Cite

View full text Add to dashboard Cite

We use complex network theory to study the differences between the friendship concepts in elementary school and university students. Four friendship networks were identified from surveys. Three of these networks are from elementary schools; two are located in the rural area of Yucatán and the other is in the urban area of Mérida, Yucatán. We analyzed the structure and the communities of these friendship networks and found significant differences among those at the elementary schools compared with those at the university. In elementary schools, the students make friends mainly in the same classroom, but there are also links among different classrooms because of the presence of siblings and relatives in the schools. These kinds of links (sibling-friend or relative-friend) are called, in this work, “mixed links”. The classification of the communities is based on their similarity with the classroom composition. If the community is composed principally of students in different classrooms, the community is classified as heterogeneous. These kinds of communities appear in the elementary school friendship networks mainly because of the presence of relatives and siblings. Once the links between siblings and relatives are removed, the communities resembled the classroom composition. On the other hand, the university students are more selective in choosing friends and therefore, even when they have friends in the same classroom, those communities are quite different to the classroom composition. Also, in the university network, we found heterogeneous communities even when the presence of sibling and relatives is negligible. These differences made up a topological structure quite different at different academic levels. We also found differences in the network characteristics. Once these differences are understood, the topological structure of the friendship network and the communities shaped in an elementary school could be predicted if we know the total number of students and the ties between siblings and relatives. However, at the university, we cannot do the same. This discovery implies that friendship is a dynamic concept that produces several changes in the friendship network structure and the way that people make groups of friends; it provides the opportunity to give analytic support to observational studies. Communities were also studied by gender and we found that when the links among relatives and siblings were removed, the number of communities formed by one gender alone increased. At the university, many communities formed by students of the same gender were also found.

show abstract

Section: Methodsmentioning

confidence: 99%

“…In this work we use the data presented in the paper entitled Modeling Social Network Topologies in Elementary Schools [ 27 ]. In that work, the authors conducted a survey of children at three elementary schools, including all the grades from first to sixth, and determined the topological structure of networks that arose from that survey.…”

Section: Introductionmentioning

confidence: 99%

Friendship Concept and Community Network Structure among Elementary School and University Students

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Stehle et al built a human contact network of primary school students by collecting face-to-face contact data, and found that the structure of the network was characterized by clusters and modularization, which could not be described by an ideal random network model [1] . Huerta-Quintanilla et al studied the friendship network and enemy network of primary school students, and found that the degree of nodes in the friendship network followed Poisson distribution, and the network had high clustering coefficient, while the degree of nodes in the enemy network followed Power law distribution [2] . By collecting human contact data in an office building, Genois et al found the relatively isolated offices led to community structure in the contact network, with far more connections within communities than between them.…”

Section: Introductionmentioning

confidence: 99%

“…At present, there are still relatively little research on the structure of human contact networks in the real world, and stay at the level of simple structural characteristics such as degree †This is corresponding author. distribution and clustering coefficients, and the network structure is not explored deeply enough [1,2,14] . Therefore, we need to conduct more in-depth empirical research on the structure of human contact networks.…”

Section: Introductionmentioning

confidence: 99%

Structural statistics of a human contact network in a research institute

Liu

Yi-na

Zhu

2020

Proceedings of the 6th ACM SIGSPATIAL International Workshop on Emergency Management Using GIS

View full text Add to dashboard Cite

Human contact networks are important ways for the spread of infectious diseases, and it is of great significance to study the structure of human contact networks in the real world. In this paper, the human contact data in a research institute of a university is collected using video surveillance devices, and the corresponding human contact network is constructed. On this basis, the structural characteristics of the human contact network are analyzed at the micro and macro levels respectively, and compared with the ER random network, WS small-world network and BA scale-free network. The results show that the human contact network cannot be accurately described by the three commonly used ideal networks, but it still has some structural characteristics of each ideal network. The contact network has more similarities in structure with the WS small-world network, including large clustering coefficient, modularity and average shortest path length, at the same time, it has more hierarchies and weak assortativity. This study can provide experimental support for the construction of network models in epidemic spreading dynamics, and is of great significance for the study of mechanism and control measures of epidemic spreading in complex networks.

show abstract

“…Ndeffo Mbah et al [ 37 ] studied the effects of imitation behavior and contact heterogeneity in social contact networks on vaccination coverage. Huerta-Quintanilla et al [ 25 ] modeled social contacts based on social networks in three elementary school case studies. Szell et al [ 43 ] examined the social contact patterns in a large online social network.…”

Section: Introductionmentioning

confidence: 99%

Inferring a District-Based Hierarchical Structure of Social Contacts from Census Data

Liu

Zhu

2015

PLoS ONE

View full text Add to dashboard Cite

Researchers have recently paid attention to social contact patterns among individuals due to their useful applications in such areas as epidemic evaluation and control, public health decisions, chronic disease research and social network research. Although some studies have estimated social contact patterns from social networks and surveys, few have considered how to infer the hierarchical structure of social contacts directly from census data. In this paper, we focus on inferring an individual’s social contact patterns from detailed census data, and generate various types of social contact patterns such as hierarchical-district-structure-based, cross-district and age-district-based patterns. We evaluate newly generated contact patterns derived from detailed 2011 Hong Kong census data by incorporating them into a model and simulation of the 2009 Hong Kong H1N1 epidemic. We then compare the newly generated social contact patterns with the mixing patterns that are often used in the literature, and draw the following conclusions. First, the generation of social contact patterns based on a hierarchical district structure allows for simulations at different district levels. Second, the newly generated social contact patterns reflect individuals social contacts. Third, the newly generated social contact patterns improve the accuracy of the SEIR-based epidemic model.

show abstract

Modeling Social Network Topologies in Elementary Schools

Cited by 13 publications

References 30 publications

Friendship Concept and Community Network Structure among Elementary School and University Students

Friendship Concept and Community Network Structure among Elementary School and University Students

Structural statistics of a human contact network in a research institute

Inferring a District-Based Hierarchical Structure of Social Contacts from Census Data

Contact Info

Product

Resources

About