ArticlePDF Available


Social Networks Nowadays In a famous experiment, Stanley Milgram showed in 1969 that, on the average, 6 links (therefore passing through 5 intermediates) were sufficient to connect two strangers in the United States. These six degrees of separation have been observed by other studies but also carrying on small samples. The advent of social networks on the Internet has recently allowed checks on a massive scale. Studies of the Microsoft instant messaging, Twitter or Facebook confirmed and even amplified the small world hypothesis. Thus, by analyzing the 69 billion relationships between the 721 million people having logged into Facebook in May 2011, it appears that the average distance (or number of degrees) between two randomly selected users on the planet is 4.7. Various explanations of the small world are discussed in this article (weak ties between clusters, the presence of hubs in a scale-free structure) and appear in the end rather complementary. JEL classification : Z1, Z19
... Therefore, we decided to experiment with alternative sampling methods, especially different network sampling strategies. Network sampling methods comprise a set of different methodological approaches (see Heckathorn & Cameron, 2017), all of which are underpinned by a simple idea: There is a finite and small number of links between every individual of the same society (Forsé, 2012). Thus, if you ask somebody to ask somebody to ask somebody to place you in touch with one of the people whom he or she knows, you will be able to potentially reach any person, from any starting point, through only a few iterations. ...
Full-text available
Vulnerable populations are often hard to reach. Ethnic or sexual minorities, people living at the margins of social, economic, educational or occupational systems and structures are frequently experiencing precariousness, dealing with unsure material and symbolic resources. However, those populations are most of the time concealed and unseen in big statistical surveys, precisely as they stand at the edges of main social groups. Therefore, it seems very worthwhile to develop specific tool to conduct surveys on those most vulnerable populations, and mixed methods approaches are particularly relevant for that purpose. In this chapter, we will mainly rely on the Musicians LIVES survey conducted between 2012 and 2016 on “ordinary musicians” (nor rich or famous) in French-speaking Switzerland. From the sampling method to the data collection, we crossed quantitative and qualitative approaches in order to get significant and accurate results on a very informal occupational space. The chapter will be enhanced by different example and cases from other surveys conducted within the NCCR LIVES over the last decade.
... However, all these studies have always been applied on small population [12], [13]. In May 2011, 69 billion connections between 721 million individuals active on Facebook have been analysed and the average distance between two randomly selected profiles has been estimated to 4.7 [14]. In 2015, with the increasing number of OSNs' users, we expect this average to continue decreasing. ...
What do social networks do to social networks ? Has the development of digital “social networks” and more generally of means of communication accompanied changes in the actual systems of interpersonal relationships, that is, “social networks” as understood by network analysts ? This article reviews this question by outlining the foundations of the research stream called social network analysis, particularly in its study of “personal networks” (the relationships of a single person). Through the examination of a body of recent work, it shows that in many respects the question still does not have a clear answer. These studies nevertheless seem to point towards a slight regression in strong or lasting ties and an increase in weaker or more transient ties, along with the reinforcement of l’entre-soi, at least for strong ties ; that is, a better equipped, better connected world, with denser, more continuous and faster communication, though perhaps with a more segregating effect.
p>Aujourd'hui, nouer des amitiés, développer des relations professionnelles ou encore constituer un couple passe, pour un nombre croissant d'individus, par Internet. Pourtant, la croyance ingénue selon laquelle cette technologie serait, par nature, désocialisante persiste. Tout internaute serait-il aspiré dans une " réalité virtuelle " ? Eloigné de son monde, de ses proches, de son corps même, renaîtrait-il dans un cyberespace désincarné ? Ce mythe masque les liens étroits du réel et du virtuel, et fait fi de l'impossibilité de séparer pratiques sociales et usages informatiques. Continuer à penser le Web comme un espace qui transcende notre réalité est une erreur d'évaluation lourde de conséquences théoriques et politiques. Car les pratiques informatiques relèvent bien souvent du détournement : les usagers domestiquent les ordinateurs et s'en emparent pour explorer de nouveaux possibles, personnels ou collectifs. Nourri d'interviews et de témoignages de blogueurs, d'artistes, d'adeptes du sexe en ligne, de figures de la militance Internet, cet ouvrage montre que la sociabilité du Web se combine de manière multiple et complexe avec les liaisons amoureuses ou amicales, les relations de parenté et les rapports de travail. Si cette reconfiguration de notre être en société ne va pas sans risques, elle est aussi porteuse de surprises : sous le regard du sociologue, le Web invente des modalités neuves et fécondes du lien social. </p
We show that renormalization group (RG) theory applied to complex networks are useful to classify network topologies into universality classes in the space of configu-rations. The RG flow readily identifies a small-world/fractal transition by finding a trivial stable fixed point of a complete graph, and two unstable fixed points consisting of (i) a pure fractal topology and (ii) a fractal with short-cuts that exists exactly at the small-world/fractal transition. As a collateral, The RG technique explains the co-existence of the seemingly contradicting fractal and small-world phases and allows to extract information on the distribution of short-cuts in real-world networks, a problem of importance for information flow in the system. A generic property that is usually inherent in scale-free networks but applies equally well to other types of networks, such as in Erd˝ os-Rényi random graphs, is the small-world feature [1, 2]. In small-world networks a very small number of steps is required to reach a given node starting from any other node. This is expressed by the slow (logarithmic) increase of the average diameter of the network, ¯ r, with the total number of nodes N 0 , ¯ r ∼ ln N 0 , where r is the shortest distance between two nodes through network links. The small-world property has been shown to apply in many empirical studies of diverse systems. However, re-cent work [3–6] showed that many networks that have been found to display the small-world property, such as the WWW, are indeed fractal, indicating a power-law dependence of the distances with the network size, ¯ r ∼ N 1/dB , where d B is the fractal dimension. Therefore, it is not clear how it is possible that fractal scale-free net-works coexist with the small world property. This shows the need for a mathematical framework that reconciles these two seemingly contradictory aspects, fractality and the small-world property.
From the Internet to networks of friendship, disease transmission, and even terrorism, the concept--and the reality--of networks has come to pervade modern society. But what exactly is a network? What different types of networks are there? Why are they interesting, and what can they tell us? In recent years, scientists from a range of fields--including mathematics, physics, computer science, sociology, and biology--have been pursuing these questions and building a new "science of networks." This book brings together for the first time a set of seminal articles representing research from across these disciplines. It is an ideal sourcebook for the key research in this fast-growing field. The book is organized into four sections, each preceded by an editors' introduction summarizing its contents and general theme. The first section sets the stage by discussing some of the historical antecedents of contemporary research in the area. From there the book moves to the empirical side of the science of networks before turning to the foundational modeling ideas that have been the focus of much subsequent activity. The book closes by taking the reader to the cutting edge of network science--the relationship between network structure and system dynamics. From network robustness to the spread of disease, this section offers a potpourri of topics on this rapidly expanding frontier of the new science.