In the influence maximization problem, we search for a k sized seed set that maximizes the expected information spread over the entire graph. In the target aware influence maximization problem, topics are added and the k sized seed set that maximize the expected information spread to nodes interested in the topic is searched. The sketch-based state-of-the-art algorithms that solve this problem are slow. Therefore, three new metric-based algorithms are presented in this thesis that solve the problem faster but are slightly less accurate. The presented algorithms are then evaluated together with the state-of-the-art algorithms for their runtime and the quality of their seed sets. For the evaluation the Pokec-Graph was used.