Academic journal article Informing Science: the International Journal of an Emerging Transdiscipline

Social Networks in Which Users Are Not Small Circles

Academic journal article Informing Science: the International Journal of an Emerging Transdiscipline

Social Networks in Which Users Are Not Small Circles

Article excerpt

Introduction

According to Cohen (2009), "the essence of the Informing Science philosophy is the transfer of knowledge from one field to another: breaking down disciplinary boundaries that hinder the flow of knowledge" [italics added]. Naturally, this transfer requires a "delivery system" (Cohen, 2009), --and this is established on the mechanism of networking. Here, networking refers to the communal linkages between members as the principle of connection between them regardless whether these constituents are computers, humans, or robots. The members of a network are deliberately linked with each other and have roles to play in achieving the purposely constructed goals of the network. Networks form patterns of contact in the broadest sense, and these contacts can move from one point in a network to another or can be cocreated by network members (Monge & Contractor, 2003). It can be argued that each discipline (Cohen, 2009) is based on a network that facilitates the transfer of knowledge in that discipline. Accordingly, networking in these networks is the role of informing science.

Informing Science is the union of aspects of these disciplines, the aspects that relate to informing clients. Its purpose is to inform these disciplines. By union, I mean more than just summing all the work. There is synergy in bringing together researchers from diverse fields to bear on the common problem of how best to inform clients. (Cohen, 2009)

Specifically, this paper deals with social networks as "informing networks" (Rambe & Ng'ambi, 2011) that intercross the disciplines of networking (online social networks, OSNs), computing, behavioral and social sciences, diagrammatic modeling, and graph theory.

Understanding of social network structure has important implications for many aspects of computer science and software engineering. First, studies of user behaviors allow the performance of existing systems to be evaluated and lead to better site design (Burke, Marlow, & Lento, 2009; Wilson, Boe, Sala, Puttaswamy, & Zhao, 2009) and to applications such as ad placement policies (Williamson, 2007). Second, accurate models of user behavior in OSNs are crucial in social studies as well as in online areas such as marketing. For instance, marketers might want to exploit models of user interactions to spread their content or promotions quickly and widely (Leskovec, Adamic, & Huberman, 2007; Watts & Peretti, 2007). Third, understanding how the workload of social networks is reshaping the Internet is valuable when designing next-generation infrastructure and content distribution systems (Krishnamurthy, 2009; Rodriguez, 2009). In this context, recent studies have examined these patterns by using data gathered from online social sites, for instance, writing messages to other users (Chun, 2008; Huberman, Romero, & Wu, 2009; Viswanath, Mislove, Cha, & Gummadi, 2009; Wilson et al., 2009), or by accessing third party applications (Gjoka, Sirivianos, Markopoulou, & Yang, 2008; Nazir, Raza, & Chuah, 2008; Viswanath et al., 2009).

The characterization of social networks has been dominated by study of topological characteristics (e.g., Ahn, Han, Kwak, Moon, & Jeong, 2007). Graph representations are analyzed in terms of connectivity using techniques such as degree distribution, diameter, average degree, clustering coefficient, average path length, and the degree of loops or cycles. According to Gong et al. (2012), traditional social network studies are based mostly on the topology of the network, i.e., a user is a node, and a relationship (e.g., friendship) is represented by a link. Statistics are used to describe general trends; however, in social networks, statistics are not completely satisfactory in the sense that they cannot account for individual events. Graph metrics that characterize the connectivity structure have only limited use since "one can produce a set of synthetic graphs which have the exact same metrics or statistics but exhibit fundamentally different connectivity structures" (Oregon Network Research Group [ONRG], 2014). …

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