Citation networks contain timely information about what researchers are interested in at a certain time. A community in such a network is built around either a renowned researcher or a common research field; either way, predicting how the community will change in the future will give insight into the research trend in the future. The paper proposes and analyzes methods to predict how communities change over time in the citation network graph without additional external information and based on link prediction and community detection. Different combinations of the proposed methods are also analyzed. Experiments show that the proposed methods can predict the citation community changes multiple timeframes in the future. Furthermore, the performance of the methods differs based on the prediction time span.