Analyzing the Strength of Co-authorship Ties with Neighborhood Overlap

Brandão, Michele A.; Moro, Mirella M.

doi:10.1007/978-3-319-22849-5_37

Cited by 11 publications

(19 citation statements)

References 20 publications

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“…Another limitation is applying tieness in co-authorship social networks from research areas in which collaborations among researchers are not a common practice. For example, in the sociology area, the level of collaboration is low [4]. Nonetheless, this is a limitation intrinsic to the definition of co-authorship networks, which should contain a good number of connections for any proper analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Many studies address tie strength in social networks [4,6,8,16,17,31]. Following Granovetter's theory [17], ties are weak when they serve as bridges in the network by connecting users from different groups, and strong when they link individuals in the same group.…”

Section: Related Workmentioning

confidence: 99%

“…(ki(ki−1)) , where e i is the number of edges between all neighbors [4,33] Normalized direct social weight…”

Section: Clustering Coefficient 2eimentioning

confidence: 99%

“…Such strength may also be measured by using the neighborhood overlap metric [4,13], a topological property that captures the total number of collaborations between the two ends of each edge and identifies the edges forming bridges in a community (set of nodes that are densely connected). The advantages of using such metric are its simple computation, the possibility to identify if ties are bridges or not, and the inclusion of neighbors to calculate tie strength (then allowing to analyze how a tie is in the social network, for example, isolated or not).…”

Section: Introductionmentioning

confidence: 99%

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The strength of co-authorship ties through different topological properties

Brandão

Moro

2017

J Braz Comput Soc

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Social networks are complex structures that describe individuals (graph nodes) connected in any social context (graph edges). Different metrics can be applied to those networks and their properties in order to understand behavior and even predict the future. One of such properties is tie strength, which allows to identify prominent individuals, analyze how relationships play different roles, predict links, and so on. Here, we specifically address the problem of measuring tie strength in co-authorship social networks (nodes are researchers and edges represent their co-authored publications). We start by presenting four cases that emphasize the problems of current metrics. Then, we propose a new metric for tie strength, called tieness, that is simple to calculate and better differentiates the degrees of strength. Accompanied with a nominal scale, tieness also provides better results when compared to the existing metrics. Our analyses consider three real social networks built from publications collected from digital libraries on Computer Science, Medicine, and Physics. Finally, we also make all datasets publicly available.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…(ki(ki−1)) , where e i is the number of edges between all neighbors [4,33] Normalized direct social weight…”

Section: Clustering Coefficient 2eimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The strength of co-authorship ties through different topological properties

Brandão

Moro

2017

J Braz Comput Soc

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“…He presents the importance of weak ties in SNs for various aspects, such as the spread of information. Since then, the strength of ties has been studied in different contexts with distinct goals, e.g., [Brandão and Moro 2015, Lopes et al 2011, Silva et al 2014. However, few studies have addressed the strength of ties in temporal social networks [Dasgupta et al 2008, Karsai et al 2014, Kostakos 2009, Nicosia et al 2013.…”

Section: Introductionmentioning

confidence: 99%

Universidade Federal de Minas Gerais - Belo Horizonte - MG

Brandão¹,

Moro²

2017

Anais Do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM)

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The study of social ties has lead to building rigorous models that reveal the evolution of social networks and their dynamism. In this context, a central aspect is the strength of ties, which allows the study of the roles of relationships. Here, besides analyzing the strength of co-authorship ties, we also present a set of metrics and algorithms to measure such strength. Initial studies of social networks have emphasized the importance of properly measuring the strength of social ties to understand social behaviors [Granovetter 1973, Newman 2001]. Also, the study of social ties is fundamental for building rigorous models that reveal the evolution of social networks (SN) and the dynamics of social exchange [Aiello et al. 2014]. More recently, analyzing tie strength has allowed to investigate the roles of relationships including ranking for influence detection [Freire and Figueiredo 2011], as well as its influence in communication patterns [Wiese et al. 2015] and team formation [Castilho et al. 2017].

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