S Weerasinghe (2013) Analytical frameworks of social network analysis, in Network Biology, Theories, Methods and Applications, WJ ZHANG (Eds)Nova Science Publishers, Inc. New York, USA pp. 1:25. Page 1 Analytical frameworks of social network analyses Swarna Weerasinghe Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada E-mail: swarna.weerasinghe@dal.ca Abstract Existing relationships and connections among humans, animals and micro or macro organisms can be explored, analyzed and interpreted using “social” network methodologies. Though the term social implies the human (within societal) relationships, ties and interactions, the theory and applications of social networks can be extended to a wide spectrum disciplines, consisting of biology, sociology, engineering, health, business and informatics. Emerging cyber space human communications through interactions on facebook, twitter and blog spaces, have added new dimensions, where dynamic information exchange and related text analytical methods have become prominent components in social network analytical frameworks. Within the scope of this chapter, the term social networking is applicable, when the existing connections are dynamic, in the sense the network connectionsare used for exchangesand transactionssuch astransportation, information diffusion, disease transmission social interactions. In the literature the term, social network is loosely defined to indicate static networks where there are no dynamic interactions among the actors. On the other hand, dynamic networks are defined as those changes over time or over geographical spaces (Kolaczyk, 2009).The analytical framework described in this chapter adopts the definition of social networks that do not change over time and geographical space. Scope of this chapter is to guide the reader through analytical frameworks that are common across disciplines. The chapter starts from simple subject-to-subject data collection methods and then expands on to biological and health network data processing, organization and analyses.Albeit the focus is mainly on dynamic networks, applicable static network structures are also described in this chapter. The chapter ends with a description on methods of statistical analyses that enable meaningful inferences on samples of random network data. In keeping with the scope of this book, the attention is paid exclusively on health and biological networks. Key words: analytical frameworks; social network; Text mining to meta matrix data;