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

Le Falher, Géraud; Cesa-Bianchi, Nicolò; Gentile, Claudio; Vitale, Fabio

Document type :

Rapport de recherche

Title :

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

Author(s) :

Le Falher, Géraud [Auteur]
Machine Learning in Information Networks [MAGNET]
Cesa-Bianchi, Nicolò [Auteur]
Dipartimento di Scienze dell'Informazione [Milano]
Gentile, Claudio [Auteur]
Universitá degli Studi dell’Insubria = University of Insubria [Varese] [Uninsubria]
Vitale, Fabio [Auteur]
Machine Learning in Information Networks [MAGNET]
Aalto University School of Science and Technology [Aalto, Finland]

Institution :

INRIA Lille

Publication date :

2016

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Collections :