“In social science, the structural approach that is based on the study of interaction among social actors is called social network analysis” (Freeman, 2004). Social network analysts study the structure formed by the nodes (people or group) connected by the links (relationships or flow). Traditionally, researchers in this field study such structures constructed by the society of humans, or animals such as ants, bees, or apes. They discover various patterns and examine their effects on those societies (Krebs, 2000). Social network analysis caters its contribution in economics, biology, chemistry, social psychology, information science, geography, and sociolinguistics. More recently due to growth of the Internet, social network analysis is spanning its roots in the study of the World Wide Web (WWW), email communications, computer networks, online marketing, and spread of viruses. This article provides overview of the interesting finding of this wonderful field of social networks. Note that research conducted is different from the research being done on development of social networking website such as Facebook, MySpace, etc., though analyses of social networking websites is a subfield in social network analyses.
Michele H. Jackson suggested the network analysis as a methodology to examine the WWW. He represented the WWW using the network analysis terms, such as actors, relation, network, and networks structures, suggesting that social network analysis is applicable to the network of the hyperlinks (Jackson, 1997). Han Woo Park summarizes various research related to hyperlink network analysis (HNA), and presents HNA as an emerging methodology (Park, 2003). Park’s paper implied that the use of social network analyses for analyzing the WWW is still in its primary stages and has many mysteries to reveal.
Although, the social analysis of the WWW is an emerging field it has already unveiled various interesting WWW’s phenomenon, several of them are listed by Barabási in his book “Linked: The New Science of Networks”. According to him, “Networks are present everywhere. All we need is an eye for them” (Barabási, 2003). He represents the WWW as a social network as opposed to the static web of sites. The network from by the websites is not different than other naturally formed networks such as human network or other biologically formed networks. He discusses several studies which reveal that most networks, small or large, natural or artificial, all exhibit a similar topology and properties. For instance, 90% of the documents of the web have 10 or fewer links, while the small numbers of pages are referenced by millions. He called these small numbers of pages as the hubs which keeps the network connected. Similar hubs found in network formed by food chain, chemical reactions, or airports. The WWW also observes the 80/20 rule, wherein “80 percent of links on the Web points to only 15 percent of Webpages” and also, “80 percent of citation go to only 38 percent of scientist” (Barabási, 2003). This property of the web is described as obeying the Power Law. Existence of the power law distribution implies that small events (pages with fewer links) coexist with the larger events (pages with plenty of links), and hence generate an ever decreasing curve.
According to Gonzalez-Bailon, “(Hyper) links play a twofold role on the web: they open the channels through which users access information, and they determine the centrality of sites and their visibility” (Gonzalez-Bailon, 2009). Gonzalez-Bailon characterizes the hyperlinks as the virtual medium through which information flows. Users navigate the WWW through these hyperlinks from webpage to webpage, and assimilating the information they need. But, these hyperlink networks are inherently biased and aid certain websites to become central and more visible compared to the others. Power law distribution gives rise to the occurrence called “rich-get-richer”. As the small number of the websites has received a very high percent of hyperlinks, these websites become the “gravity centers” and have tendency to attract even more links. Moreover, search engines consider the number of hyperlinks pointing to a webpage as a key criterion in determining the rank of the webpage. Therefore, the search engines further aid in this mechanism, since people have tendency to reference top results and ignoring the rest (Gonzalez-Bailon, 2009). Barabási articulates that “rich-get-richer” is governed by two laws: growth and preferential attachments. The network starts from a nucleus and grows by the addition of new nodes, and these new nodes prefer the nodes with more number of links for linking themselves (Barabási, 2003).
Barabási, A.-L. (2003). Linked: The New Science of Networks . Perseus Publishing.
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 Power law is a mathematical relationship between two quantities wherein frequency of one object varies as power of other.