On the TrollTrust Model for Edge Sign Prediction in Social Networks
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Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:402411, 2017.
Abstract
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 labels of the edges (i.e., the positive or negative nature of the social relationships). Many successful heuristics for this problem are based on the trolltrust features, estimating at each node the fraction of outgoing and incoming positive/negative edges. We show that these heuristics can be understood, and rigorously analyzed, as approximators to the Bayes optimal classifier for a simple probabilistic model of the edge labels. We then show that the maximum likelihood estimator for this model approximately corresponds to the predictions of a Label Propagation algorithm run on a transformed version of the original social graph. Extensive experiments on a number of realworld datasets show that this algorithm is competitive against stateoftheart classifiers in terms of both accuracy and scalability. Finally, we show that trolltrust features can also be used to derive online learning algorithms which have theoretical guarantees even when edges are adversarially labeled.
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