For my first mid-sized tests, I chose the United Nations family of websites. In July 2007, I obtained a list of United Nations websites from the UN System website (unsystem.org). Selecting 93 websites that had usable URLs, I collected link data by submitting over 8,500 queries to the Yahoo API and then created a matrix of all link relations between each United Nations website. The Yahoo API can sometimes make amazing exaggerations, so to simplify the results and make them more reliable, I converted the number of links from one website to another, to just a binary value showing just one link. In other words, if one website had 50 links to another, I just recorded one link. At this point, I was able to calculate a number of standard social network measures (shown in Table 1), such as the in degree centrality, closeness, between and coreness which are all explained in the box at the end of this posting.

View full size

At this point, it was not possible to visualize the network, as there were too many links and the network was too complex. To simplify the network further, I created another network matrix that only included mutually linked United Nations websites. For this successive network matrix, I sought to capture United Nations websites that had the strongest bonds, as manifest by mutually linked websites. To understand this principle, consider that if person A deems B a friend, but B does not feel the same about A, then it would be a mistake to call A and B friends. However, if both A and B consider one another friends, then we would call them friends, and deem their bond stronger than a one way friendship. Just the same, if one website links to another, and this hyperlink is not reciprocated, then it can be argued that the bond between these two websites is weaker than the bond between two websites that link to each other. To represent this concept in the United Nations website network, I only retained websites that shared a two-way, mutual bond. This resulted in a matrix of 87 United Nations websites that shared mutual links. Standard social network analysis metrics were calculated on this network in Table 2.

The network image above shows a visualization of the 87 mutually linked United Nations website, however, you need to view the diagramme in full size to appreciate the relationships. The size of websites is a function of betweeness which is a measure that aims to capture which sites have stronger brokerage roles. Thus the larger websites are more in-between others, and are considered to be better positioned to benefit from their position between disconnected parties. Spatial placement is based on the default multi dimensional scaling algorithm (that I need to look up).Colour represents the results of a core-periphery analysis which identifies the most highly inter-connected websites and the least connected websites that are dependent on the core. In other words, the red websites are like the core in-group, or inner circle, of the United Nations website network, while the blue websites are considered to be on the periphery, and outside the core network where the real action happens. In Table 1, this is called coreness.

Standard social network calculations were produced for the two primary networks, with the sites ranked in order of in degree, so that comparisons may be easily made between tables 1 and 2. Table 1 shows the simple matrix, which is based on all 93 United Nations websites that represent the full network that was analysed. Table 2 shows the mutually-linked matrix of 87 websites, and this network only includes websites that share mutual bonds with other United Nations websites.

I did not undertake this analysis for the purpose of assessment, and consequently, will avoid passing any judgement on the status or ranking of these websites. Interpretation of social network analysis measures and visualizations can be a tricky task and it’s easy to jump to conclusions without considering limitations and potential distortions. For example, it’s easy to assume the well connected nodes in the middle are ‘better’ and represent the influential ‘winners’. Likewise, it’s easy to argue the disconnected nodes are the 'bad' or ‘loser’ websites. Perhaps there are times when such judgements are permissible, but the reader must remember that these metrics lack essential information such as the cost of managing each website. If costs were calculated, the central websites may show the least impact per dollar. Regarding context, this is just a map of internal United Nations links, and does not say anything about the websites positioning outside the United Nations network. Sound judgement requires an understanding of each node, and in this case, that means being familiar with each website. For example, high ranking websites may be more a function of marketing efforts rather than links that reflect significant partnerships or substantive relations. Nonetheless, I found the results interesting and thought my former webmaster colleagues at the United Nations may also appreciate the results. I’ll leave judgement up to the reader.

Tables of social network analysis results

• Table 1: Ninety Three UN Websites
• Table 2: Eighty Seven Mutually-linked UN Websites

 

Using images or data
If you wish to reuse the images or data on this page, feel free to do so provided you cite the source or provide a hyperlink back to www.onlinesocialmarketing.com. Here is a proposed citation “Cugelman, B. (2008) “The United Nations’ Web Network (July 2007)” Retrieved from: http://www.onlinesocialmarketing.com/online-research/social-networking/united-nations-web-network.htm”