The discrete element method (DEM) is a primary computational approach in studies of non-cohesive and cohesive particulates. DEM enables investigators to quantify particle scale interactions that inform particle degradation and mechanisms. Central to the quality of these numbers are decisions made in terms of contact models [1] and the ability to characterize these models [1]. This study investigates the implication of contact models, additional contact mechanisms, and modeling parameters on the network distribution with associated magnitudes of numbers describing particle-particle interactions. This is done within the context of information chains that enable various quantities to quantify informative numbers such as force, traction, and contact area chain networks [2]. A novel statistical approach to quantify information chain networks has been previously proposed and used for calibration purposes [3]. This study builds on that approach to quantify statistical similarity and dissimilarity between information chain networks as well as distance measures to quantify the dissimilarity between information chain networks. This enables us to investigate and quantify modeling implications on the state and evolution of particle scale interactions and mechanisms. Preliminary studies suggest some profound implications of modeling choices and their actual manifestation and role in particle interactions. REFERENCES [1] Salma Ben Turkia, Daniel N. Wilke, Patrick A. Pizette, Nicolin Govender. Benefits of virtual calibration for discrete element parameter estimation from bulk experiments. Granular Matter 21, 110 (2019). [2] Daniel N. Wilke, Traction chain networks: Insights beyond force chain networks for non-spherical particle systems, Powder Technology, Volume 402, 2022. [3] Daniel N. Wilke. Statistically equivalent force chain networks and their role in discrete element modeling (DEM) calibration, 12th South African Conference on Computational and Applied Mechanics, 2020, Cape Town, South Africa, 2020.