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Lyons (2011) offered several critiques of the social network analyses of
Christakis and Fowler, including issues of confounding, model inconsistency,
and statistical dependence in networks. Here we show that in some settings,
social network analyses of the type employed by Christakis and Fowler will
still yield valid tests of the null of no social contagion, even though
estimates and confidence intervals may not be valid. In particular, we show
that if the alter's state is lagged by an additional period, then under the
null of no contagion, the problems of model inconsistency and statistical
dependence effectively disappear which allow for testing for contagion. Our
results clarify the setting in which even "flawed" social network analyses
are still useful for assessing social contagion and social
influence.

As of late, scholars of diverse disciplines observe the growing importance of social networks for sustainable development processes. However, in the social sciences concepts of Social Network Analysis (SNA) have been frequently used metaphorically for another purpose (Hwang and Moon 2009, 7). At the same time, metaphorical uses of models can be harmful. "Relying on metaphors as the foundation for policy advice can lead to results substantially different from those presumed to be likely" (Ostrom 2010). This paper argues that empirical techniques of SNA can support evidence-based decision making and policy advice. On this note, results of a theoretically based empirical study are introduced that illustrate why and how SNA provides innovative tools to foster learning processes and synergy effects, bring together key resources and technological know-how and promote advancements and the diffusion of innovative ideas. Furthermore, it is argued that SNA helps to interpret existing networks and to identify innovation potentials in order to generate new information and to reveal new options for further developments.

VanderWeele et al.'s paper is a useful contribution to the on-going
scientific conversation about the detection of contagion from purely
observational data. It is especially helpful as a corrective to some of the
more extreme statements of Lyons (2011). Unfortunately, this paper, too,
goes too far in some places, and so needs some correction itself.

'The economic importance of social interactions not mediated by the market has long been recognized. However, it is only the emergence of network analysis that has supplied a useful method of study. The editors have performed a signal service by their anthology of important articles. They have covered all the major studies and represented well the rapid emergence of a new and important field, to which they have been among the most significant contributors.'--Kenneth Arrow, Stanford University, US. This comprehensive two-volume set brings together important contributions providing fundamental economic analyses of social networks and the central roles they play in many facets of our lives. The first volume consists of classic articles that model network formation and games on networks, as well as those on the identification of peer effects from an econometric viewpoint. The second volume provides empirical analyses of network effects on labor, education, development, crime and industrial organization, as well as some laboratory and field experiments. This set of indispensable papers, with an original introduction by the editors, will prove an essential tool to researchers, scholars and practitioners involved in this field

Our uses of digital technologies like social media platforms or email leave massive amounts of behavioral traces that are most interesting for social research. Other digital technologies like cell phones allow harnessing behavioral traces for research purposes. Such Digital Behavioral Data consists of genuinely relational records which can be thought of in terms of networks. However, this kind of data requires a shift of perspective from individuals to micro events (e.g., a post on social media) as units of observation and brings established techniques like Social Network Analysis to the center stage. We argue that, using this approach, obtaining individual attributes and attitudes as well as uncovering the micro-macro dynamics of behavior by mining patterns are potentially fruitful applications. We discuss methodological challenges and conclude that social theory is a constitutive pillar for the consolidation of Computational Social Science.

Abstract
This article discusses the conceptualization of network in Manuel Castells' theory of network society and its relation to network analysis. Networks assumed a significant role in Castells' opus magnum, The Information Age trilogy, in the latter half of the 1990s. He became possibly the most prominent figure globally in adopting network terminology in social theory, but at the same time he made hardly any empirical or methodological contribution to network analysis. This article sheds light on this issue by analyzing how the network logic embraced by Castells defines the social, economic, and political relations in his theory of network society, and how such aspects of his theory relate to social network analysis. It is shown that Castells' institutional network concept is derived from the increased relevance of networks as the emerging form of social organization, epitomized by the idea of global networks of instrumental exchanges. He did not shed light on the internal dynamics of networks, but was nevertheless able to use network as a powerful metaphor that aptly portrayed his idea of the new social morphology of informational capitalism.