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In the problem of edge sign prediction, we are given a directed graph(representing a social network), and our task is to predict the binary labelsof the edges (i.e., the positive or negative nature of the socialrelationships). Many successful heuristics for this problem are based on thetroll-trust features, estimating at each node the fraction of outgoing andincoming positive/negative edges. We show that these heuristics can beunderstood, and rigorously analyzed, as approximators to the Bayes optimalclassifier for a simple probabilistic model of the edge labels. We then showthat the maximum likelihood estimator for this model approximately correspondsto the predictions of a Label Propagation algorithm run on a transformedversion of the original social graph. Extensive experiments on a number ofreal-world datasets show that this algorithm is competitive againststate-of-the-art classifiers in terms of both accuracy and scalability.Finally, we show that troll-trust features can also be used to derive onlinelearning algorithms which have theoretical guarantees even when edges areadversarially labeled.

On the Troll-Trust Model for Edge Sign Prediction in Social Networks