  The amount of collected information on data repositories has vastly increased
with the advent of the internet. It has become increasingly complex to deal
with these massive data streams due to their sheer volume and the throughput of
incoming data. Many of these data streams are mapped into graphs, which helps
discover some of their properties. However, due to the difficulty in processing
massive streaming graphs, they are summarized such that their properties can be
approximately evaluated using the summaries. gSketch, TCM, and gMatrix are some
of the major streaming graph summarization techniques. Our primary contribution
is devising kMatrix, which is much more memory efficient than existing
streaming graph summarization techniques. We achieved this by partitioning the
allocated memory using a sample of the original graph stream. Through the
experiments, we show that kMatrix can achieve a significantly less error for
the queries using the same space as that of TCM and gMatrix.
