"The hallmark of network analysis," as stated by Edward O. Laumann, "is to explain, at least in part, the behavior of network elements … by appeal to specific features of the interconnections among the elements" (1979, p. 349). Ever since numerous studies have provided evidence for the impact of social networks on individual and collective action. Prominent network studies link the structure of relationships to attitudes and behavior of actors, such as the effect of social networks on academic prestige, economic success, diffusion of ideas, business innovation, finding employment, participation in social movements or family formation. Networks can affect behavior through social capital or social support or by means of social influence. Social influence can take various forms, such as contagion through actual personal contact or diffusion through structural equivalence (Burt 1987, Valente 2010). Channels of social influence can be social learning, social pressure, subjective obligation (Bernardi 2004, Keim et al. 2009) or different ways of brokerage and structurally connecting actors (Gould/Fernandez 1994, Passy 2004, Burt 2007, Obstfeld et al. 2013). Social capital and social support, too, can take different forms and work through different mechanisms, such as direct or buffering effects of social support, feedback processes, the invisible hand of social capital (Lin/Ao 2008) or unanticipated gains of social relationships (Small 2009). Influence can work through strong relationships, as family or friendship ties, but also via weak, hierarchical or competitive ties (Granovetter 1973, Burt 1987, Marsden/Friedkin 1993, Small 2009, Lin/Erickson 2010).
Social Network Analysis comprises a multitude of data and methods to investigate network effects. Today’s increased technical possibilities, rapid methodological advancements and the growing availability of longitudinal and big data allow researchers to study networks on different scales by using various methods stemming from social research, computational and natural sciences. Besides, small-scale qualitative studies provide rich data on the practices and perceptions of individual actors, and contexts of social action.
Nevertheless, there are still a lot of open questions with regard to the processes and mechanisms of how social networks matter and the conditions and contexts of networks effects. For instance, theoretical challenges are the modeling of the source and nature of social influence and network effects, and distinguishing between effects of media, the social environment and specific influencing individuals (Kadushin 2012). Still, a basic question is how interactional networks contribute to constructing reference groups as important aspects of social comparisons and social influence (Marsden/Friedkin 1993). Methodological challenges concern establishing causality and disentangling of influence and selection processes, among others.
At the conference we will discuss theoretical, methodological and empirical challenges and advances in the study of network processes, mechanisms and effects. Especially, we would like to create the opportunity for exchange and dialogue between different, and often disconnected, theoretical perspectives and methodological approaches to research on social networks.
Keynote: Mario L. Small (Harvard University)