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Social media such as Facebook thrive with the arrival of Web 2.0. This chapter merges traditional social network analysis (SNA) with symbolic interactionism (SI) to create a hybrid method of SNA to allow researchers to study the sociability found in Facebook. The discussion begins with identifying a common ground of SNA and SI, found in Georg Simmel’s work, and then develops methodological procedures to locate cliques in Facebook networks. A visualization technique is also suggested to further single out the social forms found in Facebook.

We introduce a multi-level model of the dependence of interfirm network topologies on the distribution and commonality of information in a network and the information strategies pursued by its member firms. Network topology, information properties of the network, and firm-level action within the network form dynamic, recursive, cross-level relationships – information properties in the network determine firm-level action, which in turn impacts the network topology and information properties. We derive predictions about the kinds of information strategies that firms are likely to adopt and succeed with in different information regimes, and about the kinds and short- and long-run dynamics of network topologies expected under different information regimes. Our model sheds new light on network topologies as a dependent variable that can be explained by network-level information regimes and firm-level information strategies.

Interorganizational partner selection decisions are plagued with uncertainty. When making partnering decisions, firms strive to answer two questions: does the prospective partner have resources which can be used to generate value in the relationship; and will the partner be willing to actively share these resources and cooperate in good faith? Answers to these questions help reduce three types of uncertainty – partner capability uncertainty, partner competitiveness uncertainty and partner reliability uncertainty. For a relationship to benefit both partners, they have to possess complimentary resources of comparable quality, avoid explicit competition as well as be willing to engage in the cooperative behaviors within the confines of their relationship. In this paper, we examine the importance of prospective partners’ characteristics (differences in size, status and specialization) as well as their network characteristics (existence of a common partner and membership in the same clique) to the formation and longevity of their social relationships, as these characteristics reduce firms’ value generation and partner reliability uncertainty.

In this chapter different network operations are described in SGL, which may be useful for security applications. Basic network management mechanisms are expressed, capable of working on their own even if traditional communications and internet includings are damaged. These include network creation from scratch, finding paths between nodes, and creating routing tables (RT) allowing for shortest path communications. Also is shown the use of SGT for analysing distributed networks with social flavour by finding strong and weak components in them and changing their topology in crisis situations. Another example is how to outline different communities in a distributed social network, find their topographical centres and evaluate physical distances between them for predicting possible social conflicts, while doing this repeatedly together with simulation of spatial mobility of individuals in time.

Kinship structures in Ambridge have been analysed using social network analysis (SNA) showing a network of a ‘small world’ type with 75 individual people linked by birth or marriage. Further, the network shows four major cliques: the first two centred on Aldridge and Archer matriarchies and the second where through the marriages of the third generation the Grundies, Carters, Bellamies and Snells connect together. The chapter considers the possible futures for kinship networks in the village, arguing either a version of the status quo or The Headlam Hypothesis through which Archers assume less importance and the strength of the weak ties in the network assume more prominence.

Purpose – Organizational and work studies consistently find an interrelationship between employees' relationships with coworkers to their morale, as indicated by their satisfaction and commitment levels. This same research shows organizational climate, as indicated by levels of trust and shared values and norms in the organization, strongly benefiting from satisfied and committed employees.Methodology/approach – This chapter concretizes workplace relationships using multipanel network data from 15 Indianapolis charter school teachers.Findings – With these data, network traits related to network cohesion, strong ties, and in-group identity identified as central processes undergirding affective relationships are directly tested. A feedback loop between organizational climate and employee morale, where both mutually reinforce the other, is also discussed.Implications – These findings show that affective relationships with coworkers improve employee morale and organizational climate.

Purpose – Criminal groups have long been central to explanations of crime and deviance. Yet, challenges in measuring their dynamic and transient nature meant that group-level explanations were often displaced in favor of individual-level ones. This chapter outlines how network methods provide a powerful tool for modeling the dynamic nature of criminal groups.

Approach – The chapter starts by providing a brief introduction to social network analysis, including key concepts and terminology. The chapter then focuses on the types of relational data available to study criminal groups, and how network methods can be used to delineate group boundaries. The chapter concludes by presenting a framework for understanding group dynamics from a network perspective, describing the contributions of network analysis to theories of group processes.

Findings – Network methods have provided meaningful advances to the study of group dynamics, leading scholars to revisit assumptions about the impact of group’ structure on delinquent behavior. Network studies of group dynamics have primarily focused on the cohesion–delinquency link (within-group structure) and the social contagion of conflict (between-group structure), highlighting important opportunities for the intersection of these two inquiries.

Value – Network methods provide a means to revisit and extend theories of crime and delinquency with a focus on social structure. The unique affinity between group dynamics and network methods highlights immense opportunities for expanding the knowledge of collective trajectories.