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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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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].

Tie strength allows to classify social relationships and identify different types of them. For instance, social relationships can be classified as persistent and similar based respectively on the regularity with which they occur and the similarity among them. On the other hand, rare and somewhat similar relationships are random and cause noise in a social network, thus hiding the actual structure of the network and preventing an accurate analysis of it. In this article, we propose a method to handle social network data that exploits temporal features to improve the detection of communities by existing algorithms. By removing random relationships, we observe that social networks converge to a topology with more pure social relationships and better quality community structures.

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.

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