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The small-world phenomenon formalized in this article as the coincidence of high local clustering and short global separation, is shown to be a general feature of sparse, decentralized networks that are neither completely ordered nor completely random. Networks of this kind have received little attention, yet they appear to be widespread in the social and natural sciences, as is indicated here by three distinct examples. Furthermore, small admixtures of randomness to an otherwise ordered network can have a dramatic impact on its dynamical, as well as structural, properties—a feature illustrated by a simple model of disease transmission.

Much work in complexity theory employs agent-based models in simulations of systems of multiple agents. Agent interaction follows some standard types of network topologies. Our aim is to assess how recent advances in the statistical modeling of social networks may contribute to agent-based modeling traditions, specifically, by providing structural characterizations of a variety of network topologies. We illustrate the points by reference to a computational model for the evolution of cooperation among agents embedded in neighborhoods, and by reference to complex, real social networks defined by the ties of political support between US Senators as revealed through ties of The purpose of this paper is to contribute to the greater understanding of network topologies for complexity analyses, particularly, analyses that deploy agent based modeling strategies. In such models, the pattern of interactions between agents is crucial, and the network topology that emerges central, to the aggregate outcomes emergent from

We investigate course registration data of 18 semesters at a Korean University to portray the time evolution of students ’ positions in the network of fellow students. Apart from being a study of the social positions of students, the present work is also an example of how large-scale, time resolved, affiliation networks can be analyzed. For example we discuss the proper definitions of weights, and propose a redefined weighted clustering coefficient. Among other things, we find that the students enter the network at the center and are gradually diffusing towards the periphery. On the other hand, the ties to the classmates of the first semester (still present at the university) will, on average, become stronger as time progresses. I.

explores the social network processes involved in adolescent substance use. Over the past three decades, researchers have focused on, with increasing clarity, the specific dynamics of peer selection and peer influence in their attempts to understand how adolescents first use a substance, what compels them to continue use, and why some of them quit. However, the exact nature of interplay between those two key social processes continues to be elusive, due to the lack of both robust longitudinal network data and sophisticated network methodologies capable of addressing such data; it is only in recent years that advances in the field have improved these deficiencies. The research presented here adopts an alternative approach using a large cross-sectional data set that is not without its limitations, but still manages to produce specific parameters for selection and influence some of which are surprisingly similar to those reported in some recent work on this topic. Inferences to describe adolescent networks are drawn from partially-formed egonetwork data contained in the 1998 and 1999 survey years of the National Survey on Drug Use and Health; a modest level of precision in these analyses is achievable