On the troll-trust model for edge sign prediction in social networks
Loading...
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2017-01-01
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
402-411
Series
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, Volume 54
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.Description
Keywords
Other note
Citation
Le Falher, G, Cesa-Bianchi, N, Gentile, C & Vitale, F 2017, On the troll-trust model for edge sign prediction in social networks . in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics . Proceedings of Machine Learning Research, vol. 54, JMLR, pp. 402-411, International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, United States, 20/04/2017 . < http://proceedings.mlr.press/v54/falher17a.html >