  Despite recent effort to estimate topology characteristics of large graphs
(i.e., online social networks and peer-to-peer networks), little attention has
been given to develop a formal methodology to characterize the vast amount of
content distributed over these networks. Due to the large scale nature of these
networks, exhaustive enumeration of this content is computationally
prohibitive. In this paper, we show how one can obtain content properties by
sampling only a small fraction of vertices. We first show that when sampling is
naively applied, this can produce a huge bias in content statistics (i.e.,
average number of content duplications). To remove this bias, one may use
maximum likelihood estimation to estimate content characteristics. However our
experimental results show that one needs to sample most vertices in the graph
to obtain accurate statistics using such a method. To address this challenge,
we propose two efficient estimators: special copy estimator (SCE) and weighted
copy estimator (WCE) to measure content characteristics using available
information in sampled contents. SCE uses the special content copy indicator to
compute the estimate, while WCE derives the estimate based on meta-information
in sampled vertices. We perform experiments to show WCE and SCE are cost
effective and also ``{\em asymptotically unbiased}''. Our methodology provides
a new tool for researchers to efficiently query content distributed in large
scale networks.
