Abstract
Social network analysis can be an important help for military and criminal intelligence analysis. In real world applications, there is seldom complete knowledge about the network of interest -- we only have partial and incomplete information about the nodes and networks present. Community detection in networks is an important area of current research in social network analysis with many applications. Finding community structures is however a challenging task and despite significant effort no satisfactory method has been found. Here we study the problem of community detection in noisy and uncertain networks with missing and false edges and propose methods for detecting community structures in them. The method is based on sampling from an ensemble of certain networks that are consistent with the available information about the uncertain networks.
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