Sampling from Diffusion Networks

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.