  The diffusion phenomenon has a remarkable impact on Online Social Networks
(OSNs). Gathering diffusion data over these large networks encounters many
challenges which can be alleviated by adopting a suitable sampling approach.
The contributions of this paper is twofold. First we study the sampling
approaches over diffusion networks, and for the first time, classify these
approaches into two categories; (1) Structure-based Sampling (SBS), and (2)
Diffusion-based Sampling (DBS). The dependency of the former approach to
topological features of the network, and unavailability of real diffusion paths
in the latter, converts the problem of choosing an appropriate sampling
approach to a trade-off. Second, we formally define the diffusion network
sampling problem and propose a number of new diffusion-based characteristics to
evaluate introduced sampling approaches. Our experiments on large scale
synthetic and real datasets show that although DBS performs much better than
SBS in higher sampling rates (16% ~ 29% on average), their performances differ
about 7% in lower sampling rates. Therefore, in real large scale systems with
low sampling rate requirements, SBS would be a better choice according to its
lower time complexity in gathering data compared to DBS. Moreover, we show that
the introduced sampling approaches (SBS and DBS) play a more important role
than the graph exploration techniques such as Breadth-First Search (BFS) and
Random Walk (RW) in the analysis of diffusion processes.
