In social network analysis, it is crucial to discover a community through the retrospective decomposition of a large social graph into easily interpretable subgraphs. Four major community discovery algorithms, namely the Breadth-First Search, the Louvain, the MaxToMin, and the Propinquity Dynamics, are implemented. Their correctness was functionally evaluated in the four most widely used graphs with vastly different characteristics and a dataset retrieved from Twitter regarding cultural and natural heritage data because this platform reflects public perception about historical events through means such as advanced storytelling in users timelines. The primary finding was that the Propinquity Dynamics algorithm outperforms the other algorithms in terms of NMI for most graphs. In contrast, this algorithm with the Louvain performs almost the same regarding modularity.