Network Discovery for uncertain graphs

Network discovery involves analyzing the edge set of a graph to determine subsets of vertices that belong to a subgraph of interest. Applications include clandestine network detection and detection of botnet activity on a computer network. Network discovery performance can be degraded by uncertainty about edge existence, connection ambiguity, and confused vertex associations. This paper presents definitions and models of these different types of uncertainty, extending established models of uncertain graphs as collections of alternate hypotheses about the edge set associated with a given set of vertices. This model serves as the basis of distinct approaches to estimating graph analytic quantities whose true value is imprecisely known due to uncertainty concerning graph structure. One approach involves computing the expected value of an analytic quantity over algo-rithmically generated samples from the space of possible graph configurations. Another approach involves making computations for a single edge-weighted graph constructed to capture the overall graph uncertainty in an average sense. The proposed methods are shown to improve the performance of network discovery processing in the presence of the types of uncertainty that frequently occur in practical applications.