  This work develops the concept of temporal network epistemology model
enabling the simulation of the learning process in dynamic networks. The
results of the research, conducted on the temporal social network generated
using the CogSNet model and on the static topologies as a reference, indicate a
significant influence of the network temporal dynamics on the outcome and flow
of the learning process. It has been shown that not only the dynamics of
reaching consensus is different compared to baseline models but also that
previously unobserved phenomena appear, such as uninformed agents or different
consensus states for disconnected components. It has been also observed that
sometimes only the change of the network structure can contribute to reaching
consensus. The introduced approach and the experimental results can be used to
better understand the way how human communities collectively solve both complex
problems at the scientific level and to inquire into the correctness of less
complex but common and equally important beliefs' spreading across entire
societies.
