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While we typically count the number of people directly affected by violence, war and state action with casualty reports, we often ignore how these people are embedded in larger networks. In this paper, I estimate the number of people who know somebody who has been killed, injured or detained in the US war on terrorism in Iraq and Afghanistan, and thus likely at higher risk to joining a resistance movement. Since such numbers are constantly changing, an on-line web-calculator for the estimates is included so that readers can make their own estimates as well. Better understanding how friends and families are affected by such actions will help us understand the wider implications of military action.

"Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others"--

Sociological explanations for economic success tend toward measures of embeddedness in long-standing social institutions, such as race and gender, or personal skills represented mainly by educational attainment. Instead, we seek a distinctively social foundation for success by investigating the long-term association between high school popularity and income. Using rich longitudinal data, we find a clear and persistent association between the number of friendship nominations received and adult income, even after accounting for the mediating influences of diverse personal, family, and work characteristics. This skill is distinct from conventional personality measures such as the Big Five and persists long into adulthood. We hypothesize that popularity encapsulates a socioemotional skill recognized by peers as the practice of being a good friend rather than an indicator of social status.

Is domain-spanning beneficial? Can it promote innovation? Classic research on recombinant innovation suggests that domain-spanning fosters the accumulation of diverse information and can thus be a springboard for fresh ideas—most of which emanate from the merger of extant ideas from distinct realms. But domain-spanning is also challenging to produce and to evaluate. Here, the domains of interest are subfields. We focus on subfield spanning in sociology, a topically diverse field whose distinct subfields are still reasonably permeable. To do so, we introduce two measures of subfield integration, one of which uniquely accounts for the novelty of subfield combinations. We find (within the limits of observable data) the costs to be minimal but the rewards substantial: Once published, sociology articles that integrate subfields (especially rarely spanned subfields) garner more citations. We discuss how these results illuminate trends in the discipline of sociology and inform theories of recombinant innovation.

Visualizing data is central to social scientific work. Despite a promising early beginning, sociology has lagged in the use of visual tools. We review the history and current state of visualization in sociology. Using examples throughout, we discuss recent developments in ways of seeing raw data and presenting the results of statistical modeling. We make a general distinction between those methods and tools designed to help explore data sets and those designed to help present results to others. We argue that recent advances should be seen as part of a broader shift toward easier sharing of code and data both between researchers and with wider publics, and we encourage practitioners and publishers to work toward a higher and more consistent standard for the graphical display of sociological insights.