Detecting communities in complex networks accurately is a prime challenge, preceding further analyses of network characteristics and dynamics. Until now, community detection took into account only positively valued links, while many actual networks also feature negative links. We extend an existing Potts model to incorporate negative links as well, resulting in a method similar to the clustering of signed graphs, as dealt with in social balance theory, but more general. To illustrate our method, we applied it to a network of international alliances and disputes. Using data from 1993--2001, it turns out that the world can be divided into six power blocs similar to Huntington's civilizations, with some notable exceptions.Comment: 7 pages, 2 figures. Revised versio

Why do certain domains of knowledge grow fast while others grow slowly or stagnate? Two distinct theoretical arguments hold that knowledge growth is enhanced by knowledge specialization and knowledge brokerage. Based on the notion of recombinant knowledge growth, we show that specialization and brokerage are opposing modes of knowledge generation, the difference between them lying in the extent to which homogeneous vs. heterogeneous input ideas get creatively recombined. Accordingly, we investigate how both modes of knowledge generation can enhance the growth of technology domains. To address this question, we develop an argument that reconciles both specialization and brokerage into a dynamic explanation. Our contention is that specializing in an increasingly homogeneous set of input ideas is both more efficient and less risky than brokering knowledge. Nevertheless, specializing implies progressively exhausting available recombinant possibilities, while brokerage creates new ones. Hence, technology domains tend to grow faster when they specialize, but the more specialized they become, the more they need knowledge brokerage to grow. We cast out our argument into five hypotheses that predict how growth rates vary across technology domains.

The aim of this study is to examine the role of Dutch second grade (age 13–14) high school peer networks in mediating socioeconomic background and school type effects on smoking behavior. This study is based on a longitudinal design with two measurement waves at five different high schools, of the complete networks of second grader friendships, as well as their smoking behavior, school type, and parents’ educational level. The analysis is done by simulation investigation for empirical network analysis (SIENA) modeling that can control for friendship selection on the basis of smoking similarity when assessing friends’ influence on smoking. The findings show that, when controlling for friendship selection, the influence of friends still plays a significant role in adolescent smoking behavior, and suggests that socioeconomic background and school type effects on smoking are mediated by the friendship networks at school.

Social life clusters into many kinds of groups, a prime topic in the social sciences. Groups often coalesce around common activities, or more generally around social foci (Feld 1981;Kossinets and Watts 2009). Exchanges of information and resources are more frequent within than between groups, which tend to be connected by relatively weaker ties (Granovetter 1973). Some groups may have conflicting relations between them. Conflicts can also exist within groups, but if conflict escalates, groups typically split into opposing factions. In a given population, when neither groups nor group memberships are known beforehand, both can be inferred from social network data. Today, to detect groups or communities in networks, researchers typically use modularity optimization (Fortunato 2010;Reichardt 2009), a method that builds on block modeling (White, Boorman, and Breiger 1976).Network analysts typically assume ties are positive, even though they know not all social relations are positive. Science, for instance, is characterized by cooperation and benign disagreement, but also by epistemic rivalry. In democratic politics, disagreement with 463574A SRXXX10.1177/0003122412463574A merican Sociological ReviewBruggeman et al. 2012