Physically representative network models have been used for years for investigating pore-scale behavior in porous materials. However, the technology has remained largely in the research domain, with limited application to industrial processes. It this work, we introduce two algorithms that are derived from well-tested network modeling techniques, but provide unique capabilities for rapid assessment of important porous material properties from high-resolution 3D images. The first is a fast, fully automated algorithm for determining the characteristic length scale for three continuum parameters: porosity, permeability, and electrical resistivity (or formation factor). This information is important for assessing the size of the computational domain needed for image-based modeling. It : operates by computing relevant continuum properties from consecutively smaller subdomains extracted from a larger pore network model Resulting data trends are plotted so that the relevant characteristic lengths can be inferred. The second algorithm is a novel approach for computing the full permeability tensor, which is important for quantifying whether a material is isotropic. An artificial anisotropic porous medium was generated with a known principal flow direction not aligned with the Cartesian coordinate axes. The algorithm was validated by its ability to determine both the permeability tensor and the principal flow direction accurately and efficiently. Both network modeling algorithms were applied to networks derived from microCT images of real materials.