  Embedding networks into a fixed dimensional feature space, while preserving
its essential structural properties is a fundamental task in graph analytics.
These feature vectors (graph descriptors) are used to measure the pairwise
similarity between graphs. This enables applying data mining algorithms (e.g
classification, clustering, or anomaly detection) on graph-structured data
which have numerous applications in multiple domains. State-of-the-art
algorithms for computing descriptors require the entire graph to be in memory,
entailing a huge memory footprint, and thus do not scale well to increasing
sizes of real-world networks. In this work, we propose streaming algorithms to
efficiently approximate descriptors by estimating counts of sub-graphs of order
$k\leq 4$, and thereby devise extensions of two existing graph comparison
paradigms: the Graphlet Kernel and NetSimile. Our algorithms require a single
scan over the edge stream, have space complexity that is a fraction of the
input size, and approximate embeddings via a simple sampling scheme. Our design
exploits the trade-off between available memory and estimation accuracy to
provide a method that works well for limited memory requirements. We perform
extensive experiments on real-world networks and demonstrate that our
algorithms scale well to massive graphs.
