  Most research on predicting community evolution focuses on changes in the
states of communities. Instead, we focus on individual nodes and define the
novel problem of predicting whether a specific node stays in the same cluster,
moves to another cluster or drops out of the network. We explore variations of
the problem and propose appropriate classification features based on local and
global node measures. Motivated by the prevalence of machine learning
approaches based on embeddings, we also introduce efficiently computed
distance-based features using appropriate node embeddings. In addition, we
consider chains of features to capture the history of the nodes. Our
experimental results depict the complexity of the different formulations of the
problem and the suitability of the selected features and chain lengths.
