Yvonne Åberg

Bio: Yvonne Åberg is an academic researcher from Stockholm University. The author has contributed to research in topics: Unemployment & Population. The author has an hindex of 8, co-authored 11 publications receiving 3044 citations.

The web of human sexual contacts

Abstract: This article analyses data gathered in a 1996 Swedish survey of sexual behavior of 4781 Swedes aged 18-74 years. The authors state that the most important finding is the scale-free nature of the connectivity of an objectively defined non-professional social network. The possibility that the web of sexual contacts has a scale-free structure indicates that strategic targeting of safe-sex education campaigns t o those individuals with a large number of partners may significantly reduce the propagation of sexually transmitted diseases.

The Web of Human Sexual Contacts

Abstract: Many ``real-world'' networks are clearly defined while most ``social'' networks are to some extent subjective. Indeed, the accuracy of empirically-determined social networks is a question of some concern because individuals may have distinct perceptions of what constitutes a social link. One unambiguous type of connection is sexual contact. Here we analyze data on the sexual behavior of a random sample of individuals, and find that the cumulative distributions of the number of sexual partners during the twelve months prior to the survey decays as a power law with similar exponents $\alpha \approx 2.4$ for females and males. The scale-free nature of the web of human sexual contacts suggests that strategic interventions aimed at preventing the spread of sexually-transmitted diseases may be the most efficient approach.

Social interactions and unemployment

Abstract: This paper is concerned with social interactions and their importance for unemployment. A theoretical model is specified in which the social and psychological costs of unemployment depend upon the unemployment level. The theoretical analysis reveals social multiplier effects, and shows that multiple unemployment equilibria may emerge. Data on all 20- to 24year-olds living in the Stockholm metropolitan area during the 1990s are used to test key hypotheses derived from the model. The focus is on the role of neighborhood-based reference groups, and the results support the theoretical predictions: unemployment levels vary more across neighborhood-groups than what would be expected based on variation in observable characteristics, and individuals’ transition rates out of unemployment appear to be strongly influenced by the unemployment level within their neighborhood-based reference groups.

Individual Social Action and Macro Level Dynamics: A Formal Theoretical Model

Abstract: When large numbers of individuals interact and influence each other's choices of action, the relationship between a single individual's action and the aggregate outcome on the macro-level is far from straightforward and is therefore difficult to predict. In this paper, a theoretical model is developed that can be used to analyze how the evolution of different macro-phenomena depends upon social actions at the micro level. The model, which takes as its point of departure a set of analytical diagrams developed by T.C. Schelling (1978), is precise yet applicable to the analysis of a range of different social situations. The wide applicability of the model is demonstrated by examining two seemingly disparate social processes. One concerns the growth of a social movement and the other the prevalence of condom use in the population. Underlying both these processes is a system of interaction in which purposeful actors react to an environment that consists largely of other actors reacting in a like manner. The fo...

Statistical mechanics of complex networks

Abstract: The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

The Structure and Function of Complex Networks

Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

Statistical physics of social dynamics

Abstract: Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized.

Evolution of networks

Abstract: We review the recent rapid progress in the statistical physics of evolving networks. Interest has focused mainly on the structural properties of complex networks in communications, biology, social sciences and economics. A number of giant artificial networks of this kind have recently been created, which opens a wide field for the study of their topology, evolution, and the complex processes which occur in them. Such networks possess a rich set of scaling properties. A number of them are scale-free and show striking resilience against random breakdowns. In spite of the large sizes of these networks, the distances between most of their vertices are short - a feature known as the 'small-world' effect. We discuss how growing networks self-organize into scale-free structures, and investigate the role of the mechanism of preferential linking. We consider the topological and structural properties of evolving networks, and percolation and disease spread on these networks. We present a number of models demonstrat...