## 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 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.

Original language | English |
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Title of host publication | Proceedings of the 20th International Conference on Artificial Intelligence and Statistics |

Publisher | JMLR |

Pages | 402-411 |

Publication status | Published - 1 Jan 2017 |

MoE publication type | A4 Article in a conference publication |

Event | International Conference on Artificial Intelligence and Statistics - Hyatt Pier 66 Hotel, Fort Lauderdale, United States Duration: 20 Apr 2017 → 22 Apr 2017 Conference number: 20 |

### Publication series

Name | Proceedings of Machine Learning Research |
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Publisher | PMLR |

Volume | 54 |

ISSN (Electronic) | 2640-3498 |

### Conference

Conference | International Conference on Artificial Intelligence and Statistics |
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Abbreviated title | AISTATS |

Country | United States |

City | Fort Lauderdale |

Period | 20/04/2017 → 22/04/2017 |