  We study how to detect clusters in a graph defined by a stream of edges,
without storing the entire graph. We extend the approach to dynamic graphs
defined by the most recent edges of the stream and to several streams. The {\em
content correlation }of two streams $\rho(t)$ is the Jaccard similarity of
their clusters in the windows before time $t$. We propose a simple and
efficient method to approximate this correlation online and show that for
dynamic random graphs which follow a power law degree distribution, we can
guarantee a good approximation. As an application, we follow Twitter streams
and compute their content correlations online. We then propose a {\em search by
correlation} where answers to sets of keywords are entirely based on the small
correlations of the streams. Answers are ordered by the correlations, and
explanations can be traced with the stored clusters.
