The ultimate goal of systems biology research area is to accurately predict the behavior of biological systems through the construction of computational models, using the related molecular-level data as the input, especially when the structural information of such biological system is available. Combining the three-dimensional (3D) structural information of the cohort of macromolecules underpinning the biological system, the researchers are poised with an unprecedented opportunity to gain a full understanding on how the molecules interact with each other, particularly for an interaction network, e.g. protein-protein interaction networks. Specifically, there are currently a limited number of studies focused on the reconstruction and modelling of the structural interaction networks (SIN) between hosts-pathogens protein-protein interaction networks. In this paper, we will survey the SIN on protein-protein interactions network, in which we focus on the interactions between pathogen and host species (PHPPI). As one of the most important component of inter-species PPI study, in-depth study of PHPPI at atomic-resolution level would reveal novel insights into the underlying principles of the organization and complexity of host-pathogen PPI networks. Several related sub areas are discussed, and the related typical Big Data methods including machine learning methodologies and statistics models will also be discussed. This paper contributes to a new, yet challenging, research area in applying data analytic and machine learning technologies in bioinformatics.