In fully distributed networks data mining is an important tool for monitoring, control, and for offering personalized services to users. The underlying data model can change as a function of time according to periodic (daily, weakly) patterns, sudden changes, or long term transformations of the environment or the system itself. For a large space of the possible models for this dynamism-when the network is very large but only a few training samples can be obtained at all nodes locally-no efficient fully distributed solution is known. Here we present an approach, that is able to follow concept drift in very large scale and fully distributed networks. The algorithm does not collect data to a central location, instead it is based on online learners taking random walks in the network. To achieve adaptivity the diversity of the learners is controlled by managing the life spans of the models. We demonstrate through a thorough experimental analysis, that in a well specified range of feasible models of concept drift, where there is little data available locally in a large network, our algorithm outperforms known methods from related work.