  With social media and the according social and ubiquitous applications
finding their way into everyday life, there is a rapidly growing amount of user
generated content yielding explicit and implicit network structures. We
consider social activities and phenomena as proxies for user relatedness. Such
activities are represented in so-called social interaction networks or evidence
networks, with different degrees of explicitness. We focus on evidence networks
containing relations on users, which are represented by connections between
individual nodes. Explicit interaction networks are then created by specific
user actions, for example, when building a friend network. On the other hand,
more implicit networks capture user traces or evidences of user actions as
observed in Web portals, blogs, resource sharing systems, and many other social
services. These implicit networks can be applied for a broad range of analysis
methods instead of using expensive gold-standard information.
  In this paper, we analyze different properties of a set of networks in social
media. We show that there are dependencies and correlations between the
networks. These allow for drawing reciprocal conclusions concerning pairs of
networks, based on the assessment of structural correlations and ranking
interchangeability. Additionally, we show how these inter-network correlations
can be used for assessing the results of structural analysis techniques, e.g.,
community mining methods.
