<p>The dynamics of Mobile Social Networks (MSN) in terms of contact opportunities are critical in the new generation of networks for improving many applications such as routing, data transmission, video surveillance, community detection, and offloading. It was confirmed that utilizing the probability distribution to model the contact data could lead to improving the relevant performance metrics. The more accurate the distribution model is, the better the prediction of the contact opportunity is obtained. Two ignored challenges are addressed by this study, which also aim to empower network nodes to automate their contact data modeling. First, it is empirically demonstrated that the contact data of network nodes follow heterogeneous distributions. Second, it is mathematically proven that some generalizations of different contact type distributions do not justify even under the homogeneity assumption of distributions. By using our developed theorems and propositions, a framework called valid distribution generalization is provided. A statistical analysis is conducted to substantiate the first claim using a state-of-the-art modeling framework GAMLSS. Due to the rapid rise in the automation of operational and management systems, the feasibility of empowering nodes for adaptably model selection, rather than static or manually setting them, is investigated as well. The test of proposed hypotheses on two well-known real traces verified that using homogeneous distributions, for any reason such as simplicity or a compromise between complexity and accuracy, does not make sense in real networks. The implications of our findings on multimedia routing, data transmission and surveillance systems efforts are also discussed.</p>