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|In practice, a network visualization of a domain can be a messy one, particularly when the network is very large. Visualizations are useful to influence the powerful perceptual skills of humans, but overlapping links and labels of nodes mostly weaken this approach. It is to note that say that I cannot say researchers studying networks are completely lost. The techniques from sociology to graph theory in the literature can be found that allow analysts to detect interesting structures in networks. Analysts might pursue a tight-coupled community of the actors, or the brokers between them, or the most powerful actors – and there are a number of complex algorithms for detecting these behaviours.
It is worthwhile to mention that more mature fields in the area known as biology have developed techniques to train beginners and guarantee uniformity among analysts. The techniques are complete, so if two analysts are able to present with the same data, they most probably reach at the same conclusion. Though, in the social networks area, different networks are required to be analyzed in a different way. The wide spread of an epidemic among small towns is not necessarily the same as a spread of a business disaster on world markets (Watts D. J., 2003). From the above discussion, it is found that since there is no systematic way to understand networks; researchers need to be able to discover features to order to see patterns.
Freeman proposes that social network analysts pursue to expose two types of patterns in networks: i) those that disclose subsets of nodes that are ordered into unified social communities, and ii) those that disclose subsets of nodes that occupy comparable social roles (Freeman L. C., 2004b). A lot of work has been carried over the 6-7 decades to discover such patterns. Social Network Analysis: Methods and Applications, by Wasserman and Faust, is the main contribution used reference book for social scientists/ students (Wasserman S. and K. Faust, (1994). The book provides details and a review of SNA methods and description of the field.
|In practice, a network visualization of a domain can be a messy one, particularly when the network is large. Visualizations are useful to leverage the powerful perceptual abilities of humans, but overlapping links and illegible labels of nodes often undermine this approach. This is not to suggest that researchers studying networks are completely lost. There is a rich history of techniques from sociology to graph theory that allow analysts to find interesting features in networks. Analysts might seek a tight-knit community of individuals, or the gatekeepers between them, or the most centrally powerful entities – and there are a variety of sophisticated algorithms for finding these traits.
More mature fields, such as field biology, have developed systematic methods to train novices and ensure consistency among analysts. The methods are complete and repeatable, so if two analysts are presented with the same data, they should reach the same conclusion. However, in the social networks field, different networks need to be analyzed differently. The spread of an epidemic among villages is not necessarily the same as a spread of a financial crisis on world markets . Since there is no systematic way to interpret networks, users need to be able to flexibly explore features to discover patterns.
Freeman suggests that social network analysts seek to uncover two types of patterns in networks: (1) those that reveal subsets of nodes that are organized into cohesive social groups, and (2) those that reveal subsets of nodes that occupy equivalent social positions, or roles [FN 11]. There is a large body of work over the past 60 years to uncover such patterns. Social Network Analysis: Methods and Applications, by Wasserman and Faust, is perhaps the most widely used reference book for structural analysts [FN 33]. The book presents a review of network analysis methods and an overview of the field.
[FN 11] L. C. Freeman, "Graphic Techniques for Exploring Social Network Data", Models and Methods in Social Network Analysis, in P. J. Carrington, J. Scott and S. Wasserman, eds., Cambridge University Press, Cambridge, 2004
[FN 33] S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications, Cambridge University Press, 1994.
[FN 34] D. J. Watts, Six Degrees: The Science of a Connected Age, W.W. Norton & Company, New York, 2003.
Slight modifications. Also literature references have been copied. The source is not given anywhere in the thesis.