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.