## 6 Findings  
MDS (stress = 0.045 considered as very good goodness of fit) and two step cluster analysis results in the formation of four blog clusters. They are characterized by the property of skewness, which means that more populous clusters have smaller average incoming links values. One cluster, Cluster 0 in our analysis, contains 71 out of the 127 blogs and has and average of incoming links 1.67 which is equivalent to 1.31% of the maximum number of incoming links that it may get (max = 127). Clusters 1, 2, and 3 contain 22, 16 and 18 blogs, respectively, and they have the biggest numbers of average incoming links. In particular Cluster 3 is placed at the top of the ranking since it gets incoming links from nearly 10% in average from all the blogs (Table 1). Table 2 presents the affiliation of blogs. Regarding their affiliation Cluster 0 is a collection of blogs of heterogeneous content. Cluster 1 contains blogs with Left affiliation, while cluster 2 contains blogs with a Left or PASOK affiliation, and blogs that discuss and argue about society and democracy, digital liberties, information provision. Cluster 3 contains mainly mainstream media blogs (Table 2). In our analysis Cluster 0 is not considered as a core although it might contain influential blogs, yet it is not a set of homogeneous content blogs as regarded by bloggers in the blogosphere. Clusters 1, 2, and 3 are the cores that we were originally searching for. Since blogs within each core are linked (this is a default property of the techniques used, _i.e._, Cluster analysis on the adjacency matrix), they may be considered as sharing common properties. In this way they are distinguished by bloggers, recommended by them and hence they may be considered as highly influential groups, with distinct a content that is worth reading. \begin{table}
\begin{tabular}{|l|c|c|} \hline  & **Mean (Std. Deviation) of** & **Mean (Std. Deviation) of** \\  & **Incoming links** & **percentage \% of incoming links** \\ \hline Cluster 0 (N = 71) & 1.67 (1.44) & 1.31 (1.13) \\ \hline Cluster 1 (N = 22) & 5.68 (2.25) & 4.47 (1.77) \\ \hline Cluster 2 (N = 16) & 7.37 (2.84) & 5.80 (2.24) \\ \hline Cluster 3 (N = 18) & 11.33 (5.40) & 8.92 (4.25) \\ \hline Total & 4.45 (4.40) & 3.50 (3.46) \\ \hline \end{tabular}
\end{table} 
Table 1: Incoming links of the clusters. Table 3 presents the correlations among the four measures of influence used in the paper and number of incoming links. It serves as a preliminary test to examine whether influence measures are characterized by internal consistency. Centrality, as expressed by the normalized betweenness, is not correlated with community characteristics, such as number of comments, or with traffic. On the contrary, incoming links are correlated with centrality (for obvious reasons) and with community activity (comments) as well, while they are also correlated to a certain degree with traffic (Alexa traffic rank). Two implications arise from this table: Not all indexes of influence are correlated with each other, that is, influence is not a uniform property across all indexes, or indexes do not result in similar findings. Secondly, the number of incoming links seems to be a good indicator of influence indexes and characteristics. Applying the four measures of influence to the four clusters produces Table 4 and Figure 1. Figure 1 presents the box-plots of influence measures by clusters. It is clear that incoming links (used to construct the clusters) and normalized betweenness scale from low values in cluster 0 to the highest values of cluster 3. However there are no overall differences of the two traffic indexes among clusters 0, 1, and 2. Only Cluster 3 (Media blogs) differentiates, presenting the \"best\" values. Quite the same finding holds for the number of comments and the overall Blogosphere Authority Index. In this case the media blogs cluster presents even larger differences from the main pattern of the other clusters, because the blogs in the media blogs cluster reach a thousand comments or more for the period of the study.