Efficiently Estimating Subgraph Statistics of Large Networks in the Dark.

Exploring statistics of locally connected subgraph patterns (also known as network motifs) has helped researchers to better understand the structure and function of biological and online social networks (OSNs). Nowadays the massive size of some critical networks--often stored in already overloaded relational databases--effectively limits the rate in which nodes and edges are explored, making it challenging to accurately discover subgraph statistics. In this work, we propose to use"{\em sampling methods}" to accurately estimate subgraph statistics from as few queried nodes as possible. We present sampling algorithms that efficiently and accurately estimate subgraph properties of massive networks. Our algorithms require no pre-computation or complete network topology information. At the same time, we provide theoretical guarantees of convergence. We perform experiments using widely known data sets, and show that for the same accuracy, our algorithms require an order of magnitude less queries (samples) than the current state-of-the-art algorithms.