## 6 Findings  
### Analysis of the Social Networking and Activity Indexes of the Greek Political Blogs  
Table 1 presents the descriptive statistics of the five indexes. Incoming links range from zero to 25. That is, there exits at least one blog that gathers at the most 25 out of 127 links (20% of the links). The mean equals 4.5. Outgoing links have a wider range, from zero to 30. The mean is equal to the mean of incoming links, but the standard deviation is greater. Outgoing links have a greater variance than incoming links do, so it seems that they are used by some blogs to a greater degree as a tool of networking. Normalized betweenness ranges from zero to 7.28 with a mean equal to 1.06 and a standard deviation equal to 1.75. There is a diversity of betweenness among the blogs. The number of 1-cliques a blog belongs to ranges from zero to 80. That is, there are some blogs belonging to no 1-cliques, but some others are much more connected. Having in mind than the maximum value of 1-cliques a blog belongs to is 80, and since the mean equals 6.06, it becomes clear that most of the blogs belong to small groups of 1-cliques and only a minority of the blogs belongs to larger groups of 1-cliques. This property is associated with skewness. Skewnness is a common property of many Web 2.0 applications and it represents the situation that most the studied entities have small values of a specific characteristic while only a few have the highest values. Regarding the size of ego network, the conclusions are similar but not the same. The range is smaller than that for the 1-cliques and the mean is greater than the one for 1-cliques. Yet, it seems that skewness still holds even for this case. A first attempt to explore whether the five indexes describe different aspects of the same property, that is, connectivity of the blogs, it is usual to calculate the correlation coefficients among the indexes [23]. High correlations provide evidence that all of the indexes indeed measure aspects of the same thing. This property of indexes' inter-correlations is of the essence for the graph-theoretic study of a network and in fact it provides evidence of the reliability of the proposed indexes. Conclusions are further supported in such a case of strong index inter-correlations, and there is also strong evidence that all the indexes measure network centrality and blog connectivity. Table 2 presents the correlation coefficients among the indexes. All of them are positive and all, except one, are statistically significant. The non-significant one is the correlation between incoming links and number of 1-cliques a blog belongs to. \begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline  & **Minimum** & **Maximum** & **Mean** & **Std. deviation** \\ \hline Incoming links & 0 & 25 & 4.45 & 4.4 \\ \hline Outgoing links & 0 & 30 & 4.45 & 5.48 \\ \hline Normalized betweenness & 0 & 7.28 & 1.06 & 1.75 \\ \hline Number of 1-cliques a blog belongs to & 0 & 80 & 6.06 & 9.81 \\ \hline Size of Ego network & 0 & 38 & 8.64 & 6.72 \\ \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics of the five social network indexes. Since there is evidence that correlations exist among the proposed indexes, a usual approach is to perform Principal Components Analysis (PCA) with Varimax rotation. The method produces new variables that \"summarize\", through a statistical procedure, the original variables. Although the new variables called Principal Components (PC) or Factors do not represent some actual measurement, their values or factor scores can be used to consequently describe values of the original variables, those which constructed the factors. For example, if factor loadings are positive, large values of the factors are associated with large values of the original variables. By performing PCA two PCs are constructed, the first one explaining 53% of the total variance and the second one explaining 32% of the total variable (total variance explained = 85%).