Uncertain graph analysis plays a crucial role in many real-world applications, where the presence of uncertain information poses challenges for traditional graph mining algorithms. In this paper, we propose a novel method to find antagonistic communities in signed uncertain graphs, where vertices in the same community have a large expectation of positive edge weights and the vertices in different communities have a large expectation of negative edge weights. By restricting all the computations on small local parts of the signed uncertain graph, our method can efficiently find significant groups of antagonistic communities. We also provide theoretical foundations for the method. Extensive experiments on five real-world datasets and a synthetic dataset demonstrate the outstanding effectiveness and efficiency of the proposed method. / Thesis / Master of Science (MSc)