To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Mark Granovetter’s 1973 “The Strength of Weak Ties” (SWT) is arguably the most influential paper in sociology. The great appeal of SWT is that it links micro-processes to macro patterns, yielding a provocative, non-obvious prediction. While mostly a theory paper combining insights from multiple research areas, he offered primary empirical evidence of SWT from his labor market research on how network processes affect job finding. My goal in this brief review is to assess the current state of knowledge in labor market studies of the SWT. I particularly focus on the progress that has been made toward bolstering the causal status of SWT theory in the labor market context. I highlight three theoretical and empirical challenges that have hampered progress in in this respect: the issue of baselines, the nature of the dependent variable, and the complexity of the causal chain. I conclude by discussing promising avenues for further research in this domain.
This chapter seeks to resolve the puzzle of people’s low accuracy in perceptions of local network properties versus their much higher accuracy in perceiving global network structures. We argue that this puzzle is more apparent than real because humans rely on layers of relational schemata—mental structures dictating how social agents ought to be structurally connected—to mentally organize their social contacts. In other words, differences in accuracy reflect differences in the schemata used by the individual to mentally represent social network information at varying levels (e.g., dyadic level, triadic level, and community level). Individuals vary in their schemata repertoire, and their tendencies to adopt certain schemata in a given situation or context, so the specific set of schemata that individuals activate varies in its sufficiency and appropriateness for fully representing the network structure. We define these individual differences as network representation capacities, and review and compare four prominent approaches to quantifying them: the error paradigm, the free-recall paradigm, the structural learning paradigm and the statistical learning paradigm. We conclude by inviting researchers to reconsider the relations between cognition and egocentric networks, as well as the role of network analysis in analyzing, describing and prescribing social relational behavior.
This essay gives an outline of the main research questions of social capital theory and its foundation in the seminal paper by Coleman (1988) on the creation of human capital through social capital. The research questions address the discussions about the elements that make a network beneficial, the emergence of social capital and the relation with other resources, inequality of social capital between groups, and social capital measurements. State of the art research is presented, and the discussions in exemplary social capital research fields are summarized, such as the debate about functional communities, the Mouw-Lin debate, and the community-decline debate. The necessity of parental social capital for the creation of children’s human capital is questioned. The chapter takes stock of the research concerning these debates, sketches open questions, and provides directions for future research. In particular, the combination of different data sources and the extension of the work to new research sites seems promising.
Social networks are ubiquitous. The science of networks has shaped how researchers and society understand the spread of disease, the precursors of loneliness, the rise of protest movements, the causes of social inequality, the influence of social media, and much more. Egocentric analysis conceives of each individual, or ego, as embedded in a personal network of alters, a community partially of their creation and nearly unique to them, whose composition and structure have consequences. This volume is dedicated to understanding the history, present, and future of egocentric social network analysis. The text brings together the most important, classic articles foundational to the field with new perspectives to form a comprehensive volume ideal for courses in network analysis. The collection examines where the field of egocentric research has been, what it has uncovered, and where it is headed.
Network analysis is ubiquitous. It has shaped how researchers and society as a whole understand issues as diverse as the spread of disease, the precursors of loneliness, the rise of protest movements, the causes of social inequality, the flows of air traffic, the rise of social media, and much more (McAdam and Paulsen 1993; Wasserman and Faust 1994; Watts and Strogatz 1998; Watts 1999; Barabási 2002; Christakis and Fowler 2009; Wellman et al. 2021).1 This influence is due to the remarkable flexibility and power of network analysis. A network is simply a set of nodes and the ties between them, and a node can be anything – an individual, an organization, a website, a computer server, an airport, a nation, or any entity with the capacity to connect in any fashion to another entity. The ability to think of any relationship in network terms has proved remarkably generative for researchers.
Georg Simmel (1858-1918) is widely recognized as an important forerunner of the social network approach. This chapter discusses the impact of Simmel’s writings on the develop-ment of social network analysis and its relevance for contemporary research. I argue that Simmel’s work was both more influential and more systematic than has usually been acknow¬ledged. In the first part I trace Simmel’s influence on social network analysis by distingui¬shing between a general structural perspective and the adoption of concrete ideas, particularly formulated in his chapters on quantitative aspects and the “web of group affiliations”. In the second part the focus is on Simmel’s concept of forms of sociation (Formen der Vergesell¬schaftung). I argue that reference to so-called basic structural properties such as group size, time or space is key to an analytical perspective that provides a specific explanation of how relationships and networks matter. The “power of structural properties” with respect to the dynamics of social relationships is illustrated by a qualitative study on changes in personal networks following the loss of the spouse. I close with implications for research into personal networks.
Festinger, Schachter, and Back’s Social Pressures in Informal Groups (henceforward FSB’s SPIG) was one of the most exciting and theoretically generative works in what we now think of as the field of social networks, emerging from one of the focal arenas of Gestalt-psychology-inspired research. It established the importance of functional distance for relationship formation, and demonstrated that there were effects of variations on the scale of feet, not miles. It also used a clever research design to attempt to see if information spread along social networks. The clarity of FSB’s structuralist vision was to some degree clouded by the then-common reification of groups, and a tendency to focus on normative and functional goals to the exclusion of all else. Yet here were many of the seeds of the structural approach to social networks.
NetLab’s four East York studies in Toronto have traversed from the Community Question—how have structural shifts in society affected personal networks—to the Network Question—how have information and communication technologies (ICTs) affected the nature of these networks? Where doom-pundits had asserted that community has withered, the first two studies found community flourishing as personal networks rather than as neighborhoods, with different types of network members providing specialized support. Where recent doom-pundits warn that ICTs can weaken community, the third and fourth studies show that ICTs complement in-person contact and help networks to persist near and far. Many East Yorkers are networked individuals, using ICTs to juggle and proliferate relationships in multiple, fragmentary, far-flung networks; while others use ICTs to maintain their presence in a small number of bounded groups.
This essay addresses the emergence of the theory of social capital, describes its measurements and research, and summarizes some recent trends. It proposes two new research directions: (1) capturing culture in social capital – the study of guanxi, and (2) integrating individual and community social capital – the conceptual utility of social capital giving.
Taking up the invitation to reflect on the mid-1970s project that resulted in To Dwell Among Friends (1982), I review its development, my network survey of the 2010s, and lessons learned. This chapter discusses the decision to use egocentric network analysis as a tool to understand urban modernity in the first project and to study the effect of social ties on health in the second. The accounts spur discussions of several conceptual issues, such as the importance of considering burdensome ties, the notion of “social capital,” and the criteria for deciding a tie even exists, as well as several methodological issues, including the GSS “important matters” question, the reasons for using multiple and diverse name-eliciting questions, and the respondent burden this method creates.
How individuals try to solve problems, from simple to life-threatening ones, has been a central question across the scientific landscape. Not surprisingly, disciplines have offered theories representing their unique perspectives from cost, psychological predispositions, social status, culture, power, and even genetic inheritance. What was common across these explanations, even as larger structures or context were considered to limit or enhance action, was the focus on individuals, the primary assumption of action as decision-making or help-seeking, and an internal cost-benefit mechanism. While providing many insights, this understanding of the basis of human action falls short. The social network perspective suggests a shift to the influence of others on social action and a reconsideration of underlying assumptions. This reflection considers how applying an approach where social networks are the engine of action produced the Social Organization Strategy framework, the Network Episode Model, subsequent revisions, and the multi-level, multi-disciplinary Network Embedded Symbiome. This chapter describes how this social network perspective guides a new research effort on human well-being — the Person-to-Person Health Interview Study — and includes specific measurement batteries for ego-centric data collection.
Homophily is the higher probability of connection between similar as opposed to dissimilar entities. It is a property of social systems. It is not a synonym for “similarity” or “interpersonal liking for similar others.” In this chapter, we review the steady growth in the homophily literature citing “Birds of a Feather Flock Together“ (McPherson, Smith-Lovin, and Cook 2001). We argue that homophily has law-like properties spanning empirical domains, allowing its incorporation into a wide array of research streams across and even outside the social sciences. While we are encouraged to see an important sociological concept gain wide acceptance, we urge researchers to return to its social structural roots. Homophily is fundamentally a concept created to better understand structuration processes at various level of analysis, from interactions to organizations and beyond. We advocate a research agenda we hope will integrate homophily research through a dynamic view of social structure. We point to how new data sources and methods are poised to help bring greater integration to the enormous flock of homophily researchers.
The work by Elizabeth Bott is still mentioned as a "hypothesis" even though it has inspired many researchers over generations, pointing out that the subject is not closed. She raised a new fundamental question about the link between network structure and the forms of conjugality, which remains relevant in a world that has evolved in the social, cultural, and scientific domains. Networks are considered not only an object of research, rather they shed light on other objects (way of life, culture, behaviors, etc.). Today, Bott’s questioning may undergo some shifts as some social realities have changed and the lenses for observing these realities have also progressed. Advances in network analysis and in cultural and gender studies, as well as the more recent emphasis on relational dynamics, all contribute to further explore her question and give new insights on her hypothesis. The greater accuracy of the categories of both network structures (specifying the position of the partner) and conjugal roles (dividing them between norms and practices, then decisions and tasks) allows a more detailed description of their ways of combination. The dynamic dimension makes it possible to differentiate between contrasting processes of building a couple and integrating the partner into people's relational universes.
In their influential chapter on the boundary specification problem in network analysis, Laumann, Marsden, and Prensky (1989) argued that social network data often do not mirror the true underlying social structures in which individuals are embedded. Rather, the validity of network data hinges on the alignment of network boundaries and the social system or social mechanisms being studied. For this reason, the process of determining which actors and relationships should be included in a network is among the most critical research design issues in social network analysis, requiring a tight alliance of theory and method. Here, we build on Laumann and coauthors’ insights, updating their review with contemporary examples, and extending their ideas to the personal network research design context. We begin by identifying characteristics of personal network research, such as boundary spanning, that introduce unique challenges and opportunities to the boundary definition issue. We then apply concepts from their typology, reviewing common strategies for establishing boundaries through name generators in the context of personal network research designs.