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Binary classification problems can be naturally modeled as bipartite graphs, where we attempt to classify right nodes based on their left adjacencies. We consider the case of labeled bipartite graphs in which some labels and edges are not trustworthy. Our goal is to reduce noise by identifying and fixing these labels and edges. We first propose a geometric technique for generating random graph instances with untrustworthy labels and analyze the resulting graph properties. We focus on generatingarXiv:1811.04483v1 fatcat:znqokmyl25a2zepkqjjfsy43sm