  Partially-observed data collected by sampling methods is often being studied
to obtain the characteristics of information diffusion networks. However, these
methods usually do not consider the behavior of diffusion process. In this
paper, we propose a novel two-step (sampling/estimation) measurement framework
by utilizing the diffusion process characteristics. To this end, we propose a
link-tracing based sampling design which uses the infection times as local
information without any knowledge about the latent structure of diffusion
network. To correct the bias of sampled data, we introduce three estimators for
different categories; link-based, node-based, and cascade-based. To the best of
our knowledge, this is the first attempt to introduce a complete measurement
framework for diffusion networks. We also show that the estimator plays an
important role in correcting the bias of sampling from diffusion networks. Our
comprehensive empirical analysis over large synthetic and real datasets
demonstrates that in average, the proposed framework outperforms the common BFS
and RW sampling methods in terms of link-based characteristics by about 37% and
35%, respectively.
