The Label Propagation algorithm is a fast algorithm for finding communities in a graph, detecting these communities using network structure alone as its guide and not requiring a predefined objective function or prior information about the communities. The algorithm works by propagating labels throughout the network and forming communities based on this process of label propagation. It has been used to assign polarity of tweets, estimate potentially dangerous combinations of drugs to co-prescribe to a patient, and infer features of utterances in a dialogue for a machine learning model. The algorithm results in different community structures when run multiple times on the same graph, but can be narrowed down by assigning preliminary labels to some nodes. It has been found to have diverse applications across various domains.