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

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 proba-bilistic 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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [37 references]  Display  Hide  Download

https://hal.inria.fr/hal-01667039
Contributor : Team Magnet <>
Submitted on : Tuesday, December 19, 2017 - 9:30:19 AM
Last modification on : Friday, March 22, 2019 - 1:34:36 AM

File

aistats2017andSupp.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01667039, version 1

Citation

Géraud Le Falher, Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale. On the Troll-Trust Model for Edge Sign Prediction in Social Networks. AISTATS 2017 - 20th International Conference on Artificial Intelligence and Statistics, Apr 2017, Fort Lauderdale, United States. ⟨hal-01667039⟩

Share

Metrics

Record views

347

Files downloads

68