Using Topological Properties to Measure the Strength of Co-authorship Ties

Abstract

Studying the strength of ties in social networks allows to identify impact at micro-macro levels in the network, to analyze how distinct relationships play different roles, and so on. Indeed, the strength of ties has been investigated in many contexts with different goals. Here, we aim to address the problem of measuring ties strength in co-authorship social networks. Specifically, we present four case studies detailing problems with current metrics and propose a new one. Then, we build a co-authorship social network by using a real digital library and identify how the strength of ties relates to the quality of publication venues when measured by different topological properties. Our results show the best ranked venues have similar patterns of strength of co-authorship ties.

Tie Strength in Co-authorship Social Networks: Analyses, Metrics and a New Computational Model

“…However, by empirically analyzing the results, we identified four main problems with using solely neighborhood overlap and co-authorship frequency to measure tie strength [7,11]: (Case 1) when a pair of collaborators does not have any common neighbor, neighborhood overlap is zero; (Case 2) when determining if two collaborators are from the same community (or not), co-authorship frequency fails, as it considers only the absolute frequency of interaction; (Case 3) when there is little collaboration between a pair of collaborators and a plenty of common neighbors, neighborhood overlap and co-authorship frequency present opposite results; and (Case 4) when the results are extreme values, neighborhood overlap may not represent the reality. Hence, we proposed a new metric entitled tieness that combines a modified neighborhood overlap with co-authorship frequency [11] (also defined by Equation 5.1 in the thesis Section 5.3).…”

“…The results of this thesis appear on eleven publications: [9], [12] best paper honorable mention, [4], [6], [11], [10], [13], [7] best paper honorable mention, [8], [5] and [14]. This thesis has also contributed with a vast set of datasets 1 and a new visualization tool, called CNARe 2 .…”

Importância das Colaborações Interdisciplinares nas Redes de Coautoria Científica

“…Como trabalhos futuros, pretendemos analisar como ocorre a formação daqueles docentes que transferem conhecimento para outrasáreas através de novas colaborações [Mooney et al 2013, Silva et al 2016. Uma outra perspectiva consiste em explicitar a força das colaborações [Brandão et al 2016] para, por exemplo, destacar os principais atores (pesquisadores ou grupos de pesquisadores) que tornam uma rede mais integrada, como também para identificar aquelas colaborações que em função de sua força dão origem a novas comunidades acadêmicas [Leão et al 2017]. Além disso, evidências obtidas nos levam a especular que há um compartilhamento do conhecimento interdisciplinar implícito entre as regiões do país.…”

Universidade Federal de Minas Gerais - Belo Horizonte - MG

“…co-authorship frequency or edge weight) have been largely used to measure the strength of ties. However, through empirical analyses, we identified four main problems with using solely neighborhood overlap and co-authorship frequency to measure tie strength [Brandão et al 2016, Brandão andMoro 2017b]: (Case 1) when a pair of collaborators does not have any common neighbor, neighborhood overlap will be zero; (Case 2) when determining if two collaborators are from the same community or not, co-authorship frequency considers only the absolute frequency of interaction; (Case 3) when there is little collaboration between a pair of collaborators and manyn common neighbors, neighborhood overlap and co-authorship frequency will present opposite results; and (Case 4) when the results are extreme values, neighborhood overlap may not represent the reality.…”

“…Then, we presented a new metric called tieness [Brandão et al 2016, Brandão andMoro 2017b], which has relatively low computational cost and can be applied to other social networks types (since tieness is a topological feature). Also, the definition of tieness comes with a nominal scale that allows to identify when a tie is weak or strong and if it links researchers from different communities or not.…”