On the Troll-Trust Model for Edge Sign Prediction in Social Networks
This work addresses predicting positive or negative relationships in social networks, offering incremental improvements by providing a theoretical foundation for existing heuristics.
The paper tackled edge sign prediction in social networks by showing that troll-trust heuristics approximate the Bayes optimal classifier for a probabilistic model, and demonstrated that a Label Propagation algorithm based on this model is competitive with state-of-the-art methods in accuracy and scalability on real-world datasets.
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 troll-trust 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 real-world datasets show that this algorithm is competitive against state-of-the-art classifiers in terms of both accuracy and scalability. Finally, we show that troll-trust features can also be used to derive online learning algorithms which have theoretical guarantees even when edges are adversarially labeled.