  Interactions between users in cyberspace may lead to phenomena different from
those observed in common social networks. Here we analyse large data sets about
users and Blogs which they write and comment, mapped onto a bipartite graph. In
such enlarged Blog space we trace user activity over time, which results in
robust temporal patterns of user--Blog behavior and the emergence of
communities. With the spectral methods applied to the projection on weighted
user network we detect clusters of users related to their common interests and
habits. Our results suggest that different mechanisms may play the role in the
case of very popular Blogs. Our analysis makes a suitable basis for theoretical
modeling of the evolution of cyber communities and for practical study of the
data, in particular for an efficient search of interesting Blog clusters and
further retrieval of their contents by text analysis.
