Diffusion-Aware Sampling and Estimation in Information Diffusion Networks

Abstract—Partially-observed data collected by sampling meth-ods is often being studied to obtain the characteristics of informa-tion diffusion networks. However, these methods are usually done without considering the diffusion process behaviour. In this paper, we propose a novel two-step (sampling/estimation) measurement framework by utilizing 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 of characteristics; links-based, node-based, and cascade-based. To the best of our knowledge, this is the first study to introduce a complete framework measurements for diffusion networks. Our comprehensive empirical analysis over large synthetic and real datasets demonstrates that the proposed framework outperforms common sampling methods (BFS and RW) in terms of link-based characteristics by about 37 % and 35 % in average, respectively. We also show that an estimator has important role in correcting the bias of sampling from diffusion networks. I